AT BAU Model

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

#

# AT18 BAU Model Russia

#

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-X) (Q-HOURS) (Q-N) (Growth cycles)

#         Q       N   HOURS   XREAL       X

#

#[1,] 0.461  0.4815  0.3016 -0.4818 -0.4818

#[2,] 0.341  0.0149 -0.9248 -0.1188 -0.1188

#[3,] 0.656 -0.7195  0.2240  0.0249  0.0249

#[4,] 0.490  0.5002  0.0595  0.5031  0.5031

#

# Fraction of Variance 

#[1] 0.853 0.996 1.000 1.000 1.000

#

#

f <- matrix( c( 1.0109013858, -0.02042457, -0.02561897,  0.077221280,

                      -0.0398013001,  1.07932875,  0.18324794, -0.028891235,

                       0.0002803577, -0.01590629,  0.98814680,  0.004360747,

            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])

AT18 <- SS(F=f,H=h,K=k,z0=c(0.077221280, -0.028891235,  0.004360747,  1.0000000000),

              output.names=c("AT1","AT2","AT3"))

print(AT18)

is.SS(AT18)

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

# tfplot(simulate(AT18,sampleT=100))

AT18.data <- simulate(AT18,sampleT=100,noise=matrix(0,100,3),start=1700)

AT18.f <- forecast(l(AT18,AT18.data),horizon=150)

tfplot(AT18.f)

AIC(l(AT18,AT18.data))

shockDecomposition(m)