LA_RE

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

#

# LA_RE Latin Model Ro Americaman Empire  (RE)

#

# Measurement Matrix 

#         Q      N

#[1,] 0.707  0.707

#[2,] 0.707 -0.707

#

# Fraction of Variance 

#[1] 1 1

#

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)

f <- matrix( c( 0.9970863167, 0.1270588, -8.995648e-03,

                      -0.0001328661, 1.0004016,  5.296195e-06,

                        0.000000000,  0.00000000, 1.0000000000)

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

# To create Malthusian Random Walk uncomment next line

# f[2,2] <- 1

h <- eye(2,3)

k <- (f[,1:2,drop=FALSE])

LA_RE <- SS(F=f,H=h,K=k,z0=c(-8.995648e-03,  5.296195e-06,  1.0000000000),

              output.names=c("LA1","LA2"))

print(LA_RE)

is.SS(LA_RE)

stability(LA_RE)

tfplot(LA_RE.data <- simulate(LA_RE,sampleT=250,start=0))

#LA_RE.data <- simulate(LA_RE,sampleT=20,noise=matrix(0,20,2))

LA_RE.f <- forecast(m <- l(LA_RE,LA_RE.data),horizon=500)

tfplot(LA_RE.f)

AIC(m)

shockDecomposition(toSSChol(m))