W_E20_Model2

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# W_E20 Model2

#

#

# Cut-and-Paste Code below into window above and Run

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# Measurement Matrix  # (Growth-XREAL) (Growth-T) (T+XREAL-L) (X-N-L) (Q-N)

#          Q      N  XREAL       X       L      T

#[1,] 0.3934  0.471 -0.232  0.4614  0.4743  0.363

#[2,] 0.5140  0.146  0.742  0.0112  0.0842 -0.395

#[3,] 0.0375 -0.192  0.477 -0.0230 -0.2188  0.828

#[4,] -0.1901 -0.271  0.114  0.8864 -0.2553 -0.16265

#[5,]  0.7043 -0.599 -0.355 -0.0262 -0.1370 -0.00238

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# Fraction of Variance 

#[1] 0.717 0.925 0.982 1.000 1.000 1.000

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

require(dse)

require(matlab)

f <- matrix( c(0.999786547, -0.0240597379, -0.3345180161,  0.207659376, -0.01688057,  0.1078917882,

                      0.092242828, 1.0758906337,  0.3767707285,  -0.090040603,  3.6556120,  0.0792684816,

                     -0.008180550,  0.0498409889,  0.2846727166,  0.412570476, -0.22675544, -0.0335588961,

                     -0.011326893, -0.0884080890,  0.1189274920,  0.992370576,  0.56004578, -0.0263901081,

                      0.000730758,  0.0003718024,  0.0008210908, -0.006695602,  0.89516022, -0.0001696577,

                      0.000000000,  0.0000000000,  0.0000000000,  0.000000000,  0.00000000,  1.0000000000

),byrow=TRUE,nrow=6,ncol=6)

h <- eye(5,6)

k <- (f[,1:5,drop=FALSE])

WE20 <- SS(F=f,H=h,K=k,z0=c(0.1078917882,  0.0792684816, -0.0335588961, -0.026390108, -0.0001696577,  1.0000000000),

              output.names=c("W1","W2","W3","W4","W5"))

print(WE20)

is.SS(WE20)

stability(WE20)

# tfplot(simulate(WE20,sampleT=100))

WE20.data <- simulate(WE20,sampleT=50,noise=matrix(0,50,5))

WE20.f <- forecast(m <- l(WE20,WE20.data),horizon=50)

tfplot(WE20.f,series=1:3)

tfplot(WE20.f,series=4:5)

AIC(m)