LA_LM Model

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

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# Latin America (LA) Model (1950-2000)

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# Notice: Malthusian Exports and Export Prices

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

 #

#          Q      N XREAL      X

#[1,]  0.502  0.499 0.502  0.498

#[2,] -0.476 -0.522 0.468  0.531

#[3,] -0.157  0.105 0.718 -0.670

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# Fraction of Variance 

#[1] 0.936 0.999 1.000 1.000

#

 #Fraction of Variance 

#[1] 0.995 1.000 1.000

#

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(   1.057014434, 0.03272800, -0.2267867, 0.155122312,

                         0.046045971, 1.05667708, -0.2990176, 0.017729672,

                         0.001904368, 0.02642842,  0.9723622, 0.005552142,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next three lines

# f[1,1] <-   1.015363848; f[2,2] <-   1.01503979; f[3,3] <-  0.9340473

#

h <- eye(3,4)

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

LA_LM <- SS(F=f,H=h,K=k,z0=c(0.155122312, 0.017729672, 0.005552142,  1.0000000000),

              output.names=c("LA1","LA2","LA3"))

print(LA_LM)

is.SS(LA_LM)

stability(LA_LM)

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

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

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

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

tfplot(LA_LM.f)

AIC(m <-  l(LA_LM,LA_LM.data))

LA_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.31840994, 0.04194678, 0.0117148,  1.0000000000),

              output.names=c("LA1","LA2","LA3"))

     shockDecomposition(LA_LMx)

#

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

#[1,]  1.026223  1.057014  1.091091    0.36    0.64  0.039091 -0.001037  -0.02654

# [2,]  0.030280  0.046046  0.065626    0.35    0.65  0.021713  1.008919  46.46544

# [3,] -0.001021  0.001904  0.005069    0.42    0.58  0.003167  1.054893 333.08262

# [4,] -0.066662  0.032728  0.140321    0.31    0.69  0.104703  1.038959   9.92294

# [5,]  1.003640  1.056677  1.109185    0.27    0.73  0.059412  0.010074   0.16956

# [6,]  0.019700  0.026428  0.033571    0.38    0.62  0.008001  1.030475 128.79678

# [7,] -0.620309 -0.226787  0.106794    0.33    0.67  0.324671  1.410066   4.34306

 #[8,] -0.465998 -0.299018 -0.140359    0.26    0.74  0.149310  1.428356   9.56635

# [9,]  0.937548  0.972362  1.006288    0.19    0.81  0.029713  0.107745   3.62624

#[10,]  0.127593  0.155122  0.182920    0.51    0.49  0.034890  0.892394  25.57720

#[11,] -0.002575  0.017730  0.041207    0.49    0.51  0.023217  1.035400  44.59724

#[12,]  0.002470  0.005552  0.008123    0.53    0.47  0.003055  1.050827 343.98929

#