World-System (1640-1815) Long 17th Century

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

#

# WL17 World  Model (1640-1815)

#

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)

#

#     Measurement Matrix    (Q+N+L-T), Growth, (Q-N)

#          Q      N      L      T

#[1,] 0.5003  0.500  0.502 -0.497

#[2,] 0.0236  0.482  0.308  0.820

#[3,] 0.8489 -0.424 -0.146  0.280

#

# Fraction of Variance 

#[1] 0.987 0.995 0.999 1.000

#

f <- matrix( c(1.003915900, -0.05394886, 0.04228067,  0.034003648,

                       0.003124053 , 0.97762596, 0.01350334, -0.002133567,

                      -0.002277035, -0.02980316, 1.03872708, -0.003510017,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize, uncomment next line

# f[1,1] <- f[3,3] <- 0.96

#

h <- eye(3,4)

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

WL17 <- SS(F=f,H=h,K=k,z0=c(0.034003648, -0.002133567, -0.003510017,  1.0000000000),

              output.names=c("W1","W2","W3"))

print(WL17)

is.SS(WL17)

stability(SS(F=f[1:3,1:3,drop=FALSE],H=eye(3),R=eye(3),Q=eye(3)))

# tfplot(simulate(WL17,sampleT=100))

WL17.data <- simulate(WL17,sampleT=150,noise=matrix(0,150,3))

WL17.f <- forecast(l(WL17,WL17.data),horizon=150)

tfplot(WL17.f)

AIC(m <- l(WL17,WL17.data))

WL17x <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.034003648, -0.002133567, -0.003510017,  1.0000000000),

              output.names=c("W1","W2","W3"))

shockDecomposition(WL17x)

#

# For realistic counterfactuals, set coefficients between LCI and UCI

#

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

# [1,]  1.002347  1.003916  1.005375    0.45    0.55 0.0012153  0.0001149    0.09452

# [2,]  0.002734  0.003124  0.003522    0.49    0.51 0.0003533  1.0007449 2832.81487

# [3,] -0.002826 -0.002277 -0.001722    0.57    0.43 0.0005016  1.0061442 2006.01746

# [4,] -0.068914 -0.053949 -0.039373    0.45    0.55 0.0109027  1.0589296   97.12537

# [5,]  0.971084  0.977626  0.984347    0.46    0.54 0.0063487  0.0278975    4.39422

# [6,] -0.033542 -0.029803 -0.026225    0.50    0.50 0.0028310  1.0339581  365.22343

# [7,]  0.012500  0.042281  0.066436    0.58    0.42 0.0215661  0.9553671   44.29945

# [8,]  0.009458  0.013503  0.017062    0.51    0.49 0.0029709  0.9905356  333.40911

# [9,]  1.026455  1.038727  1.049026    0.33    0.67 0.0105911 -0.0306179   -2.89090

#[10,]  0.032456  0.034004  0.035838    0.58    0.42 0.0013177  0.9695134  735.74468

#[11,] -0.003150 -0.002134 -0.001212    0.46    0.54 0.0007165  1.0060856 1404.21314

#[12,] -0.004265 -0.003510 -0.002713    0.55    0.45 0.0006019  1.0074250 1673.85820

#