MX_LM Model

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

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# Mexico (MX) Model (1950-2000)

#

#

# Measurement Matrix     #  overall , (HOURS-N) , (Q-N)

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#          Q      N  HOURS

#[1,]  0.579  0.577  0.576

#[2,] -0.147 -0.621  0.770

#[3,]  0.802 -0.530 -0.275

#

 #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(  1.0194883298, -0.07800042, 0.1915458,  0.1165387477,

                        0.0093824710,  0.99995422, 0.6621760,  0.0005986337,

                        0.0001282613, -0.0098748,  0.8806978, -0.0005425660,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next three lines

# f[1,1] <-   1.0092934465; f[2,2] <-   0.98995468; f[3,3] <-  0.8718908

#

h <- eye(3,4)

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

MX_LM <- SS(F=f,H=h,K=k,z0=c(0.1165387477,  0.0005986337, -0.0005425660,  1.0000000000),

              output.names=c("MX1","MX2","MX3"))

print(MX_LM)

is.SS(MX_LM)

stability(MX_LM)

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

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

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

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

tfplot(MX_LM.f)

AIC(m <-  l(MX_LM,MX_LM.data))

MX_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.235198021,  0.001931387, -0.001011367,  1.0000000000),

              output.names=c("MX1","MX2","MX3"))

     shockDecomposition(MX_LMx)

#

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

# [1,]  1.007e+00  1.0194883  1.0318210    0.34    0.66 0.0096015 0.003166    0.3298

 #[2,]  8.122e-03  0.0093825  0.0108959    0.46    0.54 0.0011494 1.010159  878.8437

# [3,] -2.821e-05  0.0001283  0.0003276    0.46    0.54 0.0001435 1.019362 7102.4015

# [4,] -2.687e-01 -0.0780004  0.0885957    0.39    0.61 0.1494211 1.147215    7.6777

# [5,]  9.751e-01  0.9999542  1.0284641    0.28    0.72 0.0195296 0.031682    1.6222

# [6,] -1.241e-02 -0.0098748 -0.0076425    0.63    0.37 0.0021144 1.028830  486.5726

# [7,] -4.203e+00  0.1915458  3.4189597    0.62    0.38 2.7446313 0.320593    0.1168

# [8,]  1.479e-01  0.6621760  1.1945156    0.70    0.30 0.4031704 0.144613    0.3587

# [9,]  7.829e-01  0.8806978  0.9398796    0.48    0.52 0.0612985 0.153272    2.5004

#[10,]  1.081e-01  0.1165387  0.1269978    0.61    0.39 0.0078555 0.899337  114.4850

#[11,] -2.823e-03  0.0005986  0.0038494    0.45    0.55 0.0024863 1.019040  409.8622

#[12,] -8.125e-04 -0.0005426 -0.0003189    0.64    0.36 0.0001791 1.020003 5694.1640

#