DE_HMA (1150-1300)

# Cut-and-Paste Code below into window above and Run

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# DE_HMA World Model   (High Middle Ages 1150-1300)

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# Measurement Matrix 

#          Q      N      U

#[1,]  0.579  0.576  0.577

#[2,] -0.223  0.793 -0.567

#[3,]  0.784 -0.199 -0.588

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 #Fraction of Variance 

#[1] 0.990 0.998 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( 1.0022981931, -0.004419900, -0.001760842,  0.0130358631,

                       0.0003848243,  0.995212018,  0.041535081, -0.0009940053,

                      -0.0004372560, -0.009051723,  1.011624811, -0.0008587574,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize, Uncomment next two lines

# f[1,1] <- f[3,3] <- 0.99

# f[2,2] <- 0.99

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h <- eye(3,4)

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

DE_HMA <- SS(F=f,H=h,K=k,z0=c(  0.0130358631, -0.0009940053, -0.0008587574,  1.0000000000),

              output.names=c("DE1","DE2","DE3"))

print(DE_HMA)

is.SS(DE_HMA)

stability(DE_HMA)

tfplot(DE_HMA.data <- simulate(DE_HMA,sampleT=150,start=1150))

#DE_HMA.data <- simulate(DE_HMA.data <-sampleT=150,noise=matrix(0,150,3))

DE_HMA.f <- forecast(m <- l(DE_HMA,DE_HMA.data),horizon=150,start=1150)

tfplot(DE_HMA.f)

AIC(m)

shockDecomposition(toSSChol(m))

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# To Run Counterfactuals, set coefficients between LCI and UCI

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

# [1,]  1.0018572  1.0022982  1.0028232    0.39    0.61 3.682e-04  6.714e-05     0.1824

# [2,]  0.0002673  0.0003848  0.0005235    0.61    0.39 1.072e-04  1.002e+00  9347.5233

# [3,] -0.0005245 -0.0004373 -0.0003568    0.60    0.40 7.080e-05  1.003e+00 14163.4056

# [4,] -0.0096209 -0.0044199  0.0007225    0.51    0.49 3.681e-03  1.007e+00   273.4599

# [5,]  0.9944609  0.9952120  0.9959600    0.30    0.70 5.726e-04  7.361e-03    12.8570

# [6,] -0.0096953 -0.0090517 -0.0084360    0.44    0.56 4.519e-04  1.011e+00  2238.1750

# [7,] -0.0173332 -0.0017608  0.0095303    0.62    0.38 1.046e-02  1.002e+00    95.7468

# [8,]  0.0376142  0.0415351  0.0462534    0.35    0.65 3.267e-03  9.617e-01   294.3519

# [9,]  1.0090351  1.0116248  1.0139216    0.33    0.67 2.091e-03 -8.406e-03    -4.0191

#[10,]  0.0128211  0.0130359  0.0132901    0.48    0.52 1.940e-04  9.892e-01  5099.0522

#[11,] -0.0011605 -0.0009940 -0.0008086    0.50    0.50 1.327e-04  1.003e+00  7560.7799

#[12,] -0.0009675 -0.0008588 -0.0007431    0.57    0.43 8.021e-05  1.003e+00 12505.7807

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