#
# USL20 TECH
#
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(1.011058, 0.1774866,
0.000000, 1.0000000
),nrow=2,ncol=2, byrow=TRUE)
h <- eye(1,2)
k <- (f[1:2,1,drop=FALSE])
TECH <- SS(F=f,H=h,K=k,z0=c( 0.1774866, 1.0000000),
output.names=c("TECH"))
print(TECH)
is.SS(TECH)
stability(TECH)
# help(simulate)
TECH.data <- simulate(TECH,sampleT=100,
start=1950)
seriesNames(outputData(TECH.data)) <- c("TECH")
AIC(TECH.model <- l(TECH,TECH.data))
shockDecomposition(toSSChol(TECH.model))
tfplot(TECH.f <- forecast(TECH.model,horizon=50))
TECH.fx <- merge.forecast(TECH.f )
#
# Price of Eggs
#
f <- matrix( c(0.184328, -0.7947243,
0.000000, 1.0000000
),nrow=2,ncol=2, byrow=TRUE)
g <- matrix(c(0.06589205,
0.0000000
),nrow=2,ncol=1, byrow=TRUE)
h <- eye(1,2)
k <- (f[1:2,1,drop=FALSE])
P_EGGS <- SS(F=f,H=h,K=k,z0=c( 0.4934786, 1.0000000),
output.names=c("P(EGGS)"))
print(P_EGGS)
is.SS(P_EGGS)
stability(P_EGGS)
# help(simulate)
P_EGGS.data <- simulate(P_EGGS,sampleT=150,input=TECH.fx,
start=1950)
seriesNames(outputData(P_EGGS.data)) <- c("P(EGGS)")
tfplot(P_EGGS.data)
AIC(P_EGGS.model <- l(P_EGGS,P_EGGS.data))
shockDecomposition(toSSChol(P_EGGS.model))