China (CN2000)

#

# CN_2000 Model

#

# Measurement Matrix 

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

#[1,]          0.328             0.325          0.306         0.3306

#[2,]          0.253             0.265          0.425        -0.2289

#[3,]         -0.116            -0.236         -0.188         0.0332

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

#[1,]      0.3311         -0.225      0.3345  0.3342  0.294  0.3371

#[2,]     -0.2248          0.698      0.1679  0.1365 -0.189 -0.1116

#[3,]      0.0467          0.494      0.0878 -0.0885  0.792  0.0716

#

# Fraction of Variance 

#

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

require(dse)

require(matlab)

f <- matrix( c(0.92964512, -0.2057941,  0.22660545, 0.03978508,

                      0.05607191,  1.0690800, -0.07760423, 0.03399751,

                      0.18197097,  0.4048719,  0.59453473, 0.32399283,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize Growth

# f[1,1] <- 0.89301340

# f[2,2] <- 1.0269540

# f[3,3] <- 0.57110769

#

h <- eye(3,4)

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

CN20 <- SS(F=f,H=h,K=k,z0=c(0.03978508, 0.03399751, 0.32399283,  1.0000000000),

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

print(CN20)

is.SS(CN20)

stability(CN20)

# tfplot(simulate(CN20,sampleT=100))

CN20.data <- simulate(CN20,sampleT=100,noise=matrix(0,100,3),start=1960)

CN20.f <- forecast(l(CN20,CN20.data),horizon=100)

tfplot(CN20.f)

AIC(l(CN20,CN20.data))