North America

#

# NACL20 BAU Model  (Growth- LU), (LU), (CO2 + E - Q - M)

#

# Measurement Matrix 

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

#[1,]         0.4364             0.439         0.4419          0.444

#[2,]         0.0709             0.049         0.0248          0.119

#[3,]         0.5711             0.512        -0.4909         -0.238

#     SP.POP.TOTL SL.UEM.TOTL.ZS

#[1,]       0.444       -0.16421

#[2,]       0.102        0.98358

#[3,]      -0.337        0.00945

#

# Fraction of Variance 

#[1] 0.824 0.973 0.994 0.999 1.000 1.000

#

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

require(dse)

require(matlab)

f <- matrix( c(0.98459345,  0.03242716, -0.07593088, 0.143592850,

                      0.01351879,  0.69160748,  0.12954780, 0.020758284,

                     -0.01852557, -0.01318351,  0.92498947, 0.004671989,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

#     To Stabilize Model, Uncomment Next line

#     f[1,1] <- 0.9

#

h <- eye(3,4)

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

NAC20 <- SS(F=f,H=h,K=k,z0=c(0.143592850, 0.020758284, 0.004671989,  1.0000000000),

              output.names=c("NAC1","NC2","NAC3"))

print(NAC20)

is.SS(NAC20)

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

# tfplot(simulate(NAC20,sampleT=100))

NAC20.data <- simulate(NAC20,sampleT=20,noise=matrix(0,20,3),start=1960)

NAC20.f <- forecast(l(NAC20,NAC20.data),horizon=50)

tfplot(NAC20.f)

AIC(l(NAC20,NAC20.data))

shockDecomposition(m)

#

#            LCI Parameter       UCI P>=T[1] P< T[1] Std. Dev.

# [1,]  0.969459  0.984593  0.997735    0.38    0.62  0.011609

# [2,] -0.017532  0.013519  0.047808    0.62    0.38  0.027357

# [3,] -0.025623 -0.018526 -0.013206    0.47    0.53  0.006174

# [4,] -0.008038  0.032427  0.079976    0.59    0.41  0.035869

# [5,]  0.544557  0.691607  0.812814    0.56    0.44  0.104339

# [6,] -0.024217 -0.013184 -0.001754    0.58    0.42  0.009780

# [7,] -0.156219 -0.075931  0.009955    0.41    0.59  0.072603

# [8,] -0.044947  0.129548  0.281415    0.58    0.42  0.147035

# [9,]  0.869596  0.924989  0.973686    0.28    0.72  0.045721

#[10,]  0.128974  0.143593  0.158108    0.70    0.30  0.010725

#[11,] -0.046654  0.020758  0.092172    0.60    0.40  0.049969

#[12,] -0.004985  0.004672  0.013633    0.44    0.56  0.008505

#