MX_CA_US Model

#

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

#

# help(dse)

# help(SS)

#

# MX_CA_US BAU Growth

#

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

f <- matrix( c(  0.84622592, 0.10015399,  0.017055955, 0.1341106,

                       0.15941197 ,0.85507471, -0.008681849, 0.2023554,

                      -0.08741621, 0.09587767,  1.020424021, 0.3066352,

                       0.000000000,  0.0000000 ,0.00000, 1.0000000

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

# To stabilize, Set US to a Random Walk

# f[3,3] <- 1

# Or, equalize Growth Rates

# f[3,3] <- 0.85

# Or, also set MX to a Random Walk

# f[1,1] <- 1

# Set all to Random Walks with feedback

# f[1,1] <- f[2,2] <- f[3,3] <- 1

#

h <- eye(3,4)

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

MX_CA_US <- SS(F=f,H=h,K=k,z0=c( 0.1341106, 0.2023554, 0.3066352, 1.0000000),

output.names=c("MX1", "CA1", "US1"))

print(MX_CA_US)

#is.SS(MX_CA_US)

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

MX_CA_US.data <- simulate(MX_CA_US,sampleT=150,noise=matrix(0,150,3),start=1960)

tfplot(MX_CA_US.data)

MX_CA_US.f <- forecast(l(MX_CA_US,MX_CA_US.data),horizon=150,start=1960)

tfplot(MX_CA_US.f)

AIC(l(MX_CA_US,MX_CA_US.data))

shockDecomposition(toSSChol(MX_CA_US),shock=-1)