World-System (1300-1450)  Late Middle Ages

#

# WLMA World  Model Late Middle Ages (1300-1450)

# Cut and Paste code below into window above and Run

#

# Measurement Matrix 

#         Q      N      T

#[1,] 0.577  0.579 -0.576

#[2,] 0.648  0.105  0.755

#[3,] 0.498 -0.808 -0.314

#

#Fraction of Variance 

# 0.994 1.000 1.000

#

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(1.004773e+00, -2.590120e-03, -7.0154413,  3.965320e-02,

                        3.365493e-03,  9.981738e-01, -4.9464769, -2.619740e-05,

                         1.079369e-08,  1.740499e-06,  0.9970529,  8.863475e-08,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

WLMA <- SS(F=f,H=h,K=k,z0=c(  3.965320e-02, -2.619740e-05,  8.863475e-08,  1.0000000000),

              output.names=c("W1","W2","W3"))

print(WLMA)

is.SS(WLMA)

stability(WLMA)

# tfplot(simulate(WLMA,sampleT=150))

#WLMA.data <- simulate(WLMA,sampleT=150,noise=matrix(0,150,3),start=1300)

WLMA.data <- simulate(WLMA,sampleT=150,start=1300)

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

tfplot(WLMA.f)

AIC(m <- l(WLMA,WLMA.data))

shockDecomposition(toSSChol(m))