#
# IT_L16 Model (1450-1640) Cut-and-Paste Code into window above and Run
#
#
# Measurement Matrix (Growth-X), (U+X+Q), (Q-N))
# Q N U XREAL X
#[1,] 0.448 0.4576 0.4385 -0.446 -0.446
#[2,] 0.438 -0.0831 0.5978 0.471 0.471
#[3,] 0.297 -0.8762 0.0758 -0.263 -0.263
# Fraction of Variance
#[1] 0.953 1.000 1.000 1.000 1.000
#
AIC <- function(model) {informationTestsCalculations(model)[3]}
require(dse)
require(matlab)
f <- matrix( c(0.9924672575, 0.022889165, 0.10769180, 0.0348861133,
-0.0072966754, 1.019923872, 0.09406354, -0.0049509391,
-0.0002504032, -0.003487849, 0.99130198, 0.0007944552,
0.00000000, 0.0000000, 0.0000000, 1.0000000000
),byrow=TRUE,nrow=4,ncol=4)
h <- eye(3,4)
k <- (f[,1:3,drop=FALSE])
ITL16 <- SS(F=f,H=h,K=k,z0=c( 0.0348861133, -0.0049509391, 0.0007944552 , 1.0000000000),
output.names=c("IT1","IT2","IT3"))
print(ITL16)
is.SS(ITL16)
stability(ITL16)
# tfplot(simulate(ITL16,sampleT=150))
ITL16.data <- simulate(ITL16,sampleT=150,start=1450)
#ITL16.data <- simulate(ITL16,sampleT=150,noise=matrix(0,100,3))
ITL16.f <- forecast(l(ITL16,ITL16.data),horizon=150)
tfplot(ITL16.f)
AIC(m <- l(ITL16,ITL16.data))
shockDecomposition(toSSChol(m))