EE_LM Model

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

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# Eastern Europe (EE) Model (1950-2000)

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# Measurement Matrix  # overall, L-X, X-XREAL

#          Q       N  XREAL      X     L

#[1,]  0.470  0.4731  0.457  0.421 0.411

#[2,]  0.165  0.0617 -0.303 -0.614 0.707

#[3,] -0.226 -0.1098 -0.600  0.659 0.377

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# Fraction of Variance 

#[1] 0.891 0.986 1.000 1.000 1.000

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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( 0.978846422, 0.03014765, -0.07763416,  0.13492482,

                      -0.046537842, 1.11215805, -0.08280240, -0.03875519,

                       0.001016303, 0.08022084,  1.00623730, -0.02054166,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <- 0.94; f[2,2] <-  1.06; f[3,3] <-  0.97

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h <- eye(3,4)

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

EE_LM <- SS(F=f,H=h,K=k,z0=c( 0.13492482, -0.03875519, -0.02054166,  1.0000000000),

              output.names=c("EE1","EE2","EE3"))

print(EE_LM)

is.SS(EE_LM)

stability(EE_LM)

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

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

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

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

tfplot(EE_LM.f)

AIC(m <-  l(EE_LM,EE_LM.data))

EE_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("EE1","EE2","EE3"))

     shockDecomposition(EE_LMx)

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

# [1,]  0.969085  0.978846  0.988803    0.53    0.47  0.009049  0.0004096   0.04527

# [2,] -0.059899 -0.046538 -0.036713    0.54    0.46  0.011176  1.0258173  91.78908

# [3,] -0.006255  0.001016  0.006445    0.60    0.40  0.005996  0.9776192 163.03849

# [4,]  0.006525  0.030148  0.059080    0.62    0.38  0.022787  0.9448814  41.46609

 #[5,]  1.052710  1.112158  1.180858    0.26    0.74  0.067202 -0.1134875  -1.68874

# [6,]  0.053608  0.080221  0.108354    0.31    0.69  0.028556  0.9026048  31.60776

# [7,] -0.165802 -0.077634  0.005787    0.56    0.44  0.076752  1.0506428  13.68871

# [8,] -0.168961 -0.082802  0.020274    0.22    0.78  0.083640  1.1071794  13.23741

# [9,]  0.961693  1.006237  1.043247    0.14    0.86  0.034685 -0.0007920  -0.02283

#[10,]  0.125240  0.134925  0.146570    0.58    0.42  0.009778  0.8409310  86.00040

#[11,] -0.063313 -0.038755 -0.016103    0.50    0.50  0.021623  1.0205371  47.19760

#[12,] -0.032327 -0.020542 -0.010655    0.58    0.42  0.009857  0.9991622 101.36216

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