WE_LM Model

# Cut-and-paste code below into window above and Run

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# Western Europe (WE) Model (1950-2000)

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

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#          Q      N       U  HOURS  XREAL      X     L

#[1,] 0.3855  0.376  0.3864 -0.373  0.380  0.370 0.376

#[2,] 0.1885  0.455  0.1367  0.423 -0.363 -0.468 0.457

#[3,] 0.0505 -0.187 -0.0123  0.646 -0.106  0.702 0.205

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# Fraction of Variance 

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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)

f <- matrix( c( 1.0112882659, -0.01805751,  0.02142231,  0.18672325,

                     -0.0290870228,  1.05626768, -0.1048439,7 -0.01830366,

                       0.0007159374,  0.09543996,  0.94687040, -0.02230875,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

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# To stabilize model, uncomment next line

# f[1,1] <-   1.0011753832; f[2,2] <-  1.04570500; f[3,3] <-  0.93740170

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h <- eye(3,4)

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

WE_LM <- SS(F=f,H=h,K=k,z0=c(0.18672325, -0.01830366, -0.02230875,  1.0000000000),

              output.names=c("WE1","WE2","WE3"))

print(WE_LM)

is.SS(WE_LM)

stability(WE_LM)

WE_LM.data <- simulate(WE_LM,sampleT=100,start=1950)

#WE_LM.data <- simulate(WE_LM,sampleT=100,noise=matrix(0,100,3),start=1950)

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

WE_LM.fx <- merge.forecast(WE_LM.f)

tfplot(WE_LM.f)

AIC(m <-  l(WE_LM,WE_LM.data))

WE_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.37540690, -0.04072951, -0.04504546,  1.0000000000),

              output.names=c("WE1","WE2","WE3"))

     shockDecomposition(WE_LMx)

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#           LCI Parameter       UCI P>=T[1] P< T[1] Std. Dev.      Bias  Bias-z

 #[1,]  0.993167  1.0112883  1.029060    0.39    0.61  0.014103  0.002954   0.2095

# [2,] -0.038779 -0.0290870 -0.022908    0.59    0.41  0.006198  1.040862 167.9452

# [3,] -0.005773  0.0007159  0.007693    0.53    0.47  0.005643  1.010178 179.0100

# [4,] -0.093956 -0.0180575  0.050034    0.61    0.39  0.058321  1.017555  17.4476

# [5,]  1.018742  1.0562677  1.100521    0.26    0.74  0.035129 -0.028428  -0.8093

# [6,]  0.071993  0.0954400  0.114216    0.45    0.55  0.016471  0.918011  55.7353

# [7,] -0.132756  0.0214223  0.203621    0.62    0.38  0.137037  0.946353   6.9058

# [8,] -0.165661 -0.1048440 -0.060598    0.33    0.67  0.043168  1.135773  26.3107

# [9,]  0.889539  0.9468704  0.996140    0.31    0.69  0.044667  0.086222   1.9303

#[10,]  0.170864  0.1867233  0.205960    0.70    0.30  0.014354  0.817093  56.9244

#[11,] -0.040922 -0.0183037 -0.002514    0.56    0.44  0.014669  1.029731  70.1990

#[12,] -0.031639 -0.0223087 -0.012474    0.51    0.49  0.007816  1.034494 132.3475

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