WL20 CA Model

#

# CAL20 BAU Model (CANADA)

#

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

require(dse)

require(matlab)

#

# Measurement Matrix (Growth-LU) (LU) (CO2+EG-Q-L-N)

#

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

#[1,]        0.43643           0.43882        0.44188         0.4443

#[2,]        0.07086           0.04899        0.02476         0.1189

#[3,]        0.57112           0.51224       -0.49087        -0.2381

#     SP.POP.TOTL SL.UEM.TOTL.ZS

#[1,]      0.4442      -0.164207

#[2,]      0.1020       0.983576

#[3,]     -0.3372       0.009451

#

# Fraction of Variance 

#[1] 0.8237 0.9730 0.9943 0.9988 0.9999 1.0000

#

f <- matrix( c(0.97610884, 0.0256881, -0.1572464,  0.191769302,

                     0.01457517, 0.7704012 , 0.3648380 ,-0.035614316,

                     -0.01887314, 0.1050347,  0.7091121,  0.000382655,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

CAL20 <- SS(F=f,H=h,K=k,z0=c(0.191769302, -0.035614316,  0.000382655,  1.0000000000),

              output.names=c("CA1","CA2","CA3"))

print(CAL20)

is.SS(CAL20)

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

# tfplot(simulate(CAL20,sampleT=100))

CAL20.data <- simulate(CAL20,sampleT=50,noise=matrix(0,50,3))

CAL20.f <- forecast(l(CAL20,CAL20.data),horizon=50)

tfplot(CAL20.f)

AIC(l(CAL20,CAL20.data))

shockDecomposition(toSSChol(CAL20))