FR19 Model

# Cut-and-Paste Code Below into Window Above and Run

#

# FR19  Model

#

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

require(dse)

require(matlab)

#

# Measurement Matrix (Growth), (Q+X-N-HOURS), (XREAL-X-HOURS)

#

#          Q       N        U   HOURS    XREAL       X        L

#[1,] 0.3662  0.3834  0.38828  0.3758  0.38482  0.3625  0.38395

#[2,] 0.5633 -0.2781 -0.07562 -0.4070 -0.08613  0.5979 -0.26293

#[3,] 0.1223 -0.1060  0.08535 -0.4924  0.75876 -0.3849 -0.01215

#

 #Fraction of Variance 

#[1] 0.9451 0.9953 0.9996 0.9999 1.0000 1.0000 1.0000

#

f <- matrix( c(1.007941410, -0.01690067, -0.05130896,  8.419129e-02,

                     0.013598613,  0.99783430, -0.05297408, -5.194149e-06,

                     0.007128953,  0.02389778,  1.09549000,  6.987085e-03,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabiize, Uncomment next Line

# f[1,1] <- f[2,2] <- f[3,3] <- 0.9

#

h <- eye(3,4)

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

FR19 <- SS(F=f,H=h,K=k,z0=c(8.419129e-02, -5.194149e-06,  6.987085e-03,  1.0000000000),

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

print(FR19)

is.SS(FR19)

stability(m0 <- SS(F=f[1:3,1:3,drop=FALSE],R=eye(3),H=eye(3),Q=eye(3)))

# tfplot(simulate(FR19,sampleT=100))

FR19.data <- simulate(FR19,sampleT=100,noise=matrix(0,100,3),start=1800)

FR19.f <- forecast(m<- l(FR19,FR19.data),horizon=50)

tfplot(FR19.f)

AIC(m)

shockDecomposition(m0)