Africa

#

# SSA Model Cut and Paste code below and Run (Cmd-Enter)

#

# Measurement Matrix 

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

#[1,]        0.34762            0.3461         0.3332        0.34982

#[2,]       -0.16275           -0.2355        -0.3531       -0.15180

#[3,]        0.07203           -0.0701        -0.2855        0.02768

#     SP.POP.TOTL SL.UEM.TOTL.ZS     EF1     HDI1   GLOB1

#[1,]     0.34697         0.3007 0.34346  0.35004  0.2732

#[2,]    -0.20062         0.4039 0.27151 -0.07706  0.6981

#[3,]    -0.08363         0.7249 0.05914  0.11182 -0.5993

#

# Fraction of Variance 

#[1] 0.8932 0.9565 0.9941 0.9978 0.9992 0.9997 0.9999 1.0000 1.0000

#

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

require(dse)

require(matlab)

f <- matrix( c(1.0183603566, -0.02571595, 0.003499238,  0.146584882,

                    -0.0273328386,  1.02963120, 0.180449889, -0.015040318,

                    -0.0005446326, -0.01744892, 0.922227651,  0.003969535,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# Stabilize (Uncomment following command)

#      f[1,1] <- f[2,2] <- 0.9

#

h <- eye(3,4)

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

SSA <- SS(F=f,H=h,K=k,z0=c( 0.146584882, -0.015040318,  0.003969535,  1.0000000000),

              output.names=c("SSA1","SSA2","SSA3"))

print(SSA)

is.SS(SSA)

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

shockDecomposition(m)

# tfplot(simulate(SSA,sampleT=100),start=1960)

SSA.data <- simulate(SSA,sampleT=100,noise=matrix(0,100,3),start=1960)

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

tfplot(SSA.f)

AIC(l(SSA,SSA.data))