Marx Cyclical

#

# Marx Cyclical (Cut and paste code into window above and Run (Cmd-Enter)

#

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-R) (K+KU+R-i) (Growth-R)

#         K       i      LU      R    KU

#[1,] 0.171  0.0609  0.6273 -0.492 0.576

#[2,] 0.682 -0.4999 -0.0982  0.419 0.316

#[3,] 0.345  0.8623 -0.0929  0.313 0.175

#

# Fraction of Variance 

#[1] 0.481 0.841 0.987 1.000 1.000

#

f <- matrix( c(0.97077363,  0.08376097, 0.01752653, 0.08394119,

                    -0.15545345,  0.90251423, 0.03549574, 0.05609541,

                    -0.04580092, -0.06870228, 0.90907033, 0.02359125,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

Marx <- SS(F=f,H=h,K=k,z0=c(0.08394119, 0.05609541, 0.02359125,  1.0000000000),

              output.names=c("Marx1","Marx2","Marx3"))

print(Marx)

is.SS(Marx)

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

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

Marx.data <- simulate(Marx,sampleT=100,noise=matrix(0,100,3))

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

tfplot(Marx.f)

AIC(l(Marx,Marx.data))