Middle East Africa (MEA)

 #

# WL20R MEA Cut an pasted code into window above and Run (Cmd-Enter)

#

# Measurement Matrix 

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

#[1,]        0.41395            0.4092         0.4125        0.41635

#[2,]       -0.23198           -0.3612        -0.2160       -0.04435

#[3,]        0.05987           -0.6044         0.7081        0.17598

#     SP.POP.TOTL SL.UEM.TOTL.ZS

#[1,]     0.41630        0.37998

#[2,]     0.04581        0.87463

#[3,]    -0.31247       -0.03361

#

# Fraction of Variance 

#[1] 0.9587 0.9955 0.9989 0.9994 0.9999 1.0000

#

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

require(dse)

require(matlab)

f <- matrix( c(1.01389877,  0.013960359,  0.014314423,  0.160971128,

                  -0.035391585,  1.097936826, -0.00486692,3 -0.023976869,

                  -0.003813121, -0.009715855 , 0.919184163,  0.005569086,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize Uncomment Next command

#  f[2,2] <- 1

h <- eye(3,4)

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

MEA <- SS(F=f,H=h,K=k,z0=c(0.160971128, -0.023976869,  0.005569086,  1.0000000000),

              output.names=c("MEA1","MEA2","MEA3"))

print(MEA)

is.SS(MEA)

stability(MEA)

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

MEA.data <- simulate(MEA,sampleT=100,noise=matrix(0,100,3))

MEA.f <- forecast(l(MEA,MEA.data),horizon=100)

tfplot(MEA.f)

AIC(l(MEA,MEA.data))