#
# Ricardo Growth and Collapse (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) (Q-K-L) (Growth)
# K L Q
#[1,] 0.579 0.579 0.573
#[2,] -0.405 -0.405 0.819
#
# Fraction of Variance
#[1] 0.986 1.000 1.000
#
f <- matrix( c(0.98473990, 1.2485612, 0.002954592,
-0.01800149, 0.9864476, 0.001209771,
0.00000000, 0.0000000, 1.0000000000
),byrow=TRUE,nrow=3,ncol=3)
h <- eye(2,3)
k <- (f[,1:2,drop=FALSE])
Ricardo <- SS(F=f,H=h,K=k,z0=c(0.002954592, 0.001209771, 1.0000000000),
output.names=c("Ricardo1","Ricardo2"))
print(Ricardo)
is.SS(Ricardo)
stability(SS(F=f[1:2,1:2,drop=FALSE],H=eye(2),Q=eye(2),R=eye(2)))
# tfplot(simulate(Ricardo,sampleT=100))
Ricardo.data <- simulate(Ricardo,sampleT=100,noise=matrix(0,100,2))
Ricardo.f <- forecast(l(Ricardo,Ricardo.data),horizon=150)
tfplot(Ricardo.f)
AIC(l(Ricardo,Ricardo.data))