RU BAU Model

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

#

# RU18 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 # overall-XREAL, Q-N, N+XREAL-L

#

#         Q      N  XREAL      L

#[1,] 0.496  0.500 -0.502  0.502

#[2,] 0.830 -0.512  0.155 -0.155

#[3,] 0.254  0.698  0.473 -0.473

#

# Fraction of Variance 

#[1] 0.99 1.00 1.00 1.00

#

#

f <- matrix( c( 0.9981745742, 0.02786861, -0.05772300,  0.065343602,

                     -0.0070628173, 1.03900425, -0.07351076, -0.003793081,

                      0.0003765241, 0.01679889,  0.96282118, -0.001544634,

            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])

RU18 <- SS(F=f,H=h,K=k,z0=c(0.065343602, -0.003793081, -0.001544634,  1.0000000000),

              output.names=c("RU1","RU2","RU3"))

print(RU18)

is.SS(RU18)

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

# tfplot(simulate(RU18,sampleT=100))

RU18.data <- simulate(RU18,sampleT=100,noise=matrix(0,100,3),start=1700)

RU18.f <- forecast(l(RU18,RU18.data),horizon=150)

tfplot(RU18.f)

AIC(l(RU18,RU18.data))

shockDecomposition(m)