FRL16_BAU

#

# FR_L16 Model (1450-1640)  (Cut-and-Paste Code below and Run in window above)

#

#

#Measurement Matrix    (Growth), (Q-U-L), (L-U)

#           Q      N      U  HOURS      L

#[1,]  0.4455 0.4526  0.439  0.454  0.445

#[2,]  0.5105 0.3089 -0.613  0.235 -0.461

#[3,] -0.0141 0.0316 -0.628 -0.156  0.761

#

# Fraction of Variance 

#[1] 0.959 0.996 1.000 1.000 1.000

#

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

f <- matrix( c(.003702664, -0.003729599, 0.006072449,  0.039210961,

                    0.007264828,  1.001695191, 0.014830938,  0.000516837,

                   -0.000566718, -0.013438572, 1.003413102, -0.002557882,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

FRL16 <- SS(F=f,H=h,K=k,z0=c( 0.039210961,  0.000516837, -0.002557882,  1.0000000000),

              output.names=c("FR1","FR2","FR3"))

print(FRL16)

is.SS(FRL16)

stability(FRL16)

# tfplot(simulate(FRL16,sampleT=100))

 FRL16.data <- simulate(FRL16,sampleT=150)

#FRL16.data <- simulate(FRL16,sampleT=150,noise=matrix(0,100,3))

FRL16.f <- forecast(l(FRL16,FRL16.data),horizon=150)

tfplot(FRL16.f)

AIC(m <- l(FRL16,FRL16.data))

shockDecomposition(toSSChol(m))