CN_LM Model

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

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# China (CN) Model (1950-2000)

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# Measurement Matrix  (Growth), (X-HOURS), (HOURS-U)

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# Measurement Matrix 

#          Q      N      U  HOURS XREAL     X       L

#[1,]  0.389  0.388  0.388  0.354 0.387 0.345  0.3906

#[2,] -0.145 -0.199  0.180 -0.618 0.210 0.687 -0.0917

#[3,] -0.212 -0.275 -0.547  0.566 0.224 0.446 -0.1031

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# Fraction of Variance 

#[1] 0.933 1.000 1.000 1.000 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(0.986091451, -0.02842443, -0.4585358,  0.165226628,

                      0.034833406,  1.02993562,  0.5527579,  0.014032953,

                      0.001291216, -0.01073976,  0.8885394, -0.003541658,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <-  0.976230536; f[2,2] <- 1.01963627; f[3,3] <- 0.8796540

#

h <- eye(3,4)

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

CN_LM <- SS(F=f,H=h,K=k,z0=c(0.165226628,  0.014032953, -0.003541658,  1.0000000000),

              output.names=c("CN1","CN2","CN3"))

print(CN_LM)

is.SS(CN_LM)

stability(CN_LM)

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

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

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

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

tfplot(CN_LM.f)

AIC(m <-  l(CN_LM,CN_LM.data))

CN_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.329380291,  0.032283718, -0.006625928,  1.0000000000),

              output.names=c("CN1","CN2","CN3"))

     shockDecomposition(CN_LMx)

#

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

# [1,]  0.971425  0.986091  0.998362    0.43    0.57 0.0114745  0.001948    0.1698

# [2,]  0.029592  0.034833  0.042743    0.48    0.52 0.0056948  0.950907  166.9776

# [3,]  0.000336  0.001291  0.002214    0.43    0.57 0.0009726  0.984814 1012.6070

# [4,] -0.079432 -0.028424  0.022407    0.46    0.54 0.0393966  1.018616   25.8554

# [5,]  1.004458  1.029936  1.061731    0.29    0.71 0.0234094 -0.033634   -1.4368

# [6,] -0.013603 -0.010740 -0.008836    0.68    0.32 0.0021795  0.996616  457.2645

# [7,] -1.434932 -0.458536  0.395450    0.58    0.42 0.7782848  1.324269    1.7015

# [8,]  0.061739  0.552758  1.013950    0.71    0.29 0.3896567  0.346946    0.8904

# [9,]  0.800759  0.888539  0.958785    0.47    0.53 0.0657403  0.112482    1.7110

#[10,]  0.153784  0.165227  0.175926    0.63    0.37 0.0110575  0.816766   73.8650

#[11,] -0.005895  0.014033  0.033933    0.48    0.52 0.0154570  0.970760   62.8039

#[12,] -0.005243 -0.003542 -0.001926    0.57    0.43 0.0014669  0.989604  674.6288

#