World-System Forecasts

This website allows you to browse and run (in the window above) forecasts for countries in the World-System. R-code is available on my Google Drive.

#

# WL20W Model (Cut and Paste to Window Above, Run (Cmd-Enter)

#

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

# Measurement Matrix  (Growth) (HDI-Earths-EG) (LU+KOF-N-Earths-HDI)

#     EN.ATM.CO2E.KT EG.USE.COMM.KT.OE NY.GDP.MKTP.KD SL.TLF.TOTL.IN

#[1,]         0.3376             0.336          0.340          0.339

#[2,]        -0.0766            -0.291          0.087         -0.108

#[3,]         0.0303            -0.020          0.106         -0.272

#     SP.POP.TOTL SL.UEM.TOTL.ZS Earths WorldGlobal    HDI

#[1,]      0.3396        0.33722  0.332     0.33700  0.300

#[2,]      0.0168       -0.00225 -0.389    -0.00943  0.859

#[3,]     -0.3438        0.60303 -0.395     0.47912 -0.214

#

# Fraction of Variance 

#[1] 0.959 0.992 0.995 0.998 1.000 1.000 1.000 1.000 1.000

#

f <- matrix( c(1.012515245, 0.004650047,  0.01709644,  0.16883530,

                     0.015455666, 0.959520970, -0.15167718, -0.01127248,

                     0.005752657, 0.036998778,  1.05980297,  0.01080932,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# Uncomment to Stabilize Growth

# f[1,1] <- 0.99

# f[2,2] <- 0.94

# f[3,3] <- 1.03

h <- eye(3,4)

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

WL20W <- SS(F=f,H=h,K=k,z0=c(0.16883530, -0.01127248,  0.01080932,  1.0000000000),

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

print(WL20W)

#is.SS(WL20W)

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

# tfplot(simulate(WL20W,sampleT=100,start=1950))

WL20W.data <- simulate(WL20W,sampleT=150,noise=matrix(0,150,3),start=1950)

WL20W.f <- forecast(m <- l(WL20W,WL20W.data),horizon1=50)

tfplot(WL20W.f)

AIC(m)

WL20Wx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3),z0=c(0.16883530, -0.01127248,  0.01080932,  1.0000000000),

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

shockDecomposition(WL20Wx)

#

# Bootstrap Confidence Intervals

#

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

# [1,]  0.999042  1.012515  1.026560    0.34    0.66  0.010839  0.001886   0.1740

# [2,]  0.012251  0.015456  0.018762    0.51    0.49  0.002835  0.996743 351.6422

# [3,]  0.003176  0.005753  0.008326    0.37    0.63  0.002469  1.006875 407.7847

# [4,] -0.047283  0.004650  0.049143    0.42    0.58  0.041555  1.013374  24.3861

# [5,]  0.938014  0.959521  0.977878    0.44    0.56  0.017021  0.059066   3.4702

# [6,]  0.029809  0.036999  0.043918    0.59    0.41  0.006049  0.974077 161.0207

# [7,] -0.212004  0.017096  0.222945    0.45    0.55  0.175871  1.015463   5.7739

# [8,] -0.201945 -0.151677 -0.110399    0.56    0.44  0.041990  1.166313  27.7762

# [9,]  1.012621  1.059803  1.114966    0.23    0.77  0.044344 -0.026858  -0.6057

#[10,]  0.158064  0.168835  0.180791    0.67    0.33  0.012203  0.837650  68.6425

#[11,] -0.022312 -0.011272 -0.001087    0.48    0.52  0.008541  1.024571 119.9577

#[12,]  0.005685  0.010809  0.016581    0.37    0.63  0.004768  1.001938 210.1335

#