Marx Steady State

#

# Marx Steady State (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 (L+K+KU-i-R) (i+KU-K-R) (Growth)

#          K      i     LU      R    KU

#[1,]  0.427 -0.416 0.4947 -0.435 0.459

#[2,] -0.528  0.561 0.1465 -0.500 0.368

#[3,]  0.480  0.578 0.0987  0.488 0.432

#

# Fraction of Variance 

#[1] 0.798 0.968 0.986 0.999 1.000

#

f <- matrix( c(0.96953603, -0.08816104,  0.2053904,  0.13952039,

                      0.07085047,  0.91633408, -0.3942239, -0.03678521,

                     -0.02344302 , 0.09912290,  0.6630393,  0.03192651,

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(.13952039, -0.03678521,  0.03192651,  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))