China (CN21)

#

# CN_21 Model

#

# Measurement Matrix 

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

#[1,]          0.339            0.3378         0.3192          0.339

#[2,]          0.339            0.3626         0.5457         -0.284

#[3,]         -0.007            0.0608        -0.0215         -0.165

#     SP.POP.TOTL SL.UEM.TOTL.ZS    EF    KOF    HDI

#[1,]       0.340          0.309 0.347  0.320  0.347

#[2,]      -0.289         -0.358 0.164 -0.331 -0.164

#[3,]      -0.120          0.824 0.111 -0.492 -0.145

#

 #Fraction of Variance 

#1] 0.899 0.961 0.985 0.997 0.999 1.000 1.000 1.000 1.000

#

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

require(dse)

require(matlab)

f <- matrix( c(1.010974244, -0.03430989, -0.1324633, 0.18404826,

                     0.053244800,  1.10297379,  0.1145191, 0.03947626,

                     0.003813592,  0.06123891,  0.8051962, 0.0123210,

                     0.000000000,  0.00000000,  0.0000000, 1.00000000

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

h <- eye(3,4)

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

CN21 <- SS(F=f,H=h,K=k,z0=c(0.18404826, 0.03947626, 0.01232109, 1.00000000),

              output.names=c("CN1","CN2","CN3"))

print(CN21)

is.SS(CN21)

stability(CN21)

# tfplot(simulate(CN21,sampleT=100))

CN21.data <- simulate(CN21,sampleT=100,noise=matrix(0,100,3),start=1960)

CN21.f <- forecast(l(CN21,CN21.data),horizon=100)

tfplot(CN21.f)

AIC(l(CN21,CN21.data))