FR_AUST Model

# Cut-and-Paste code below into Window above and Run

#

# FR_AUST BAU

#

#

merge.forecast <- function (fx,n=1) {

#

# Merges a forecast with the outputdata

x <- splice(fx$pred,fx$forecast[[n]])

colnames(x) <- seriesNames(fx$data$output)

return(x)

}

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

#

require(dse)

require(matlab)

#

# Measurement Matrix                     GEG+MIL-G-GE)   (GH+G-GEG-GE)  (Growth)

#     SH.XPD.GHED.CH.ZS SE.XPD.TOTL.GB.ZS NE.CON.GOVT.ZS SE.XPD.TOTL.GD.ZS

#[1,]             0.164             0.433        -0.4477            -0.393

#[2,]             0.822            -0.357        -0.0291            -0.377

#[3,]             0.490             0.323         0.3823             0.613

#     SH.XPD.GHED.GE.ZS MS.MIL.XPND.ZS

#[1,]           -0.4701         0.4572

#[2,]            0.2115        -0.0918

#[3,]           -0.0572         0.3617

#

# Fraction of Variance 

#[1] 0.680 0.871 0.944 0.986 0.995 1.000

#

f <- matrix( c(0.94610447,  0.18841986, -0.04710148, -0.03876814,

                    -0.03822177,  0.98955261, -0.07109910,  0.03802201,

                     0.03158142, -0.06157702,  0.86025784, -0.02111634,

                      0.00000000,  0.00000000,  0.00000000,  1.00000000

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

h <- eye(3,4)

k <- f[1:4,1:3,drop=FALSE]

FR_AUST <- SS(F=f,H=h,K=k,z0=c(-0.03876814,  0.03802201, -0.02111634,1.00000000),

              output.names=c("FR_AUST1","FR_AUST2","FR_AUST3"))

print(FR_AUST)

is.SS(FR_AUST)

stability(m1 <- SS(F=f[1:3,1:3,drop=FALSE],H=eye(3),R=eye(3),Q=eye(3)))

FR_AUST.data <- simulate(FR_AUST,sampleT=50,noise=matrix(0,50,3),start=1960)

# tfplot(FR_AUST.data)

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

shockDecomposition(m1)

tfplot(FR_AUST.f)

FR_AUST.fx <- merge.forecast(FR_AUST.f)

# tfplot(FR_AUST.fx)

#

#

# FR_L20 Model

#

#

# Measurement Matrix

#

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

#[1,]       0.004201            0.3740         0.3762          0.369      0.3742

#[2,]       0.781330            0.0959        -0.0379         -0.059     -0.0241

#[3,]       0.000453            0.0585        -0.1871         -0.355     -0.2225

#     SL.UEM.TOTL.ZS     KOF     EF     HDI

#[1,]         0.3299  0.3643 0.2460  0.3748

#[2,]        -0.0912 -0.1994 0.5658 -0.0815

#[3,]         0.8719  0.0366 0.0632 -0.1437

#

#Fraction of Variance 

#[1] 0.774 0.947 0.980 0.995 0.998 0.999 1.000 1.000 1.000

#

f <- matrix( c(0.93679424, -0.109061598, -0.1033792, 0.3617278,

                    -0.02469352,  0.496499890, -0.4262340, 0.1732049,

                    -0.26084833, -0.005069804,  0.8573141, 0.6471091,

                   0.000000000,  0.000000000,  0.00000000, 1.000000

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

g <- matrix( c(-0.02128965, 0.2196958,  0.029796882,

                        0.17987253, 0.3177710,  0.028331071,

                       -0.26832112, 0.3821658, -0.009828239,

                        0.00000000, 0.0000000,  0.000000000

),byrow=TRUE,nrow=4,ncol=3)

h <- eye(3,4)

k <- f[1:4,1:3,drop=FALSE]

FRL20 <- SS(F=f,H=h,K=k,G=g,z0=c(.3051262,  0.8995846, -0.3075910,1.00000000),

              output.names=c("FR1","FR2","FR3"))

print(FRL20)

is.SS(FRL20)

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

#

FRL20.data <- simulate(FRL20,sampleT=50,input=window(FR_AUST.fx,end=2010),noise=matrix(0,50,3))

# FRL20.data <- simulate(FRL20,sampleT=50,input=window(FR_AUST.fx,end=2010))

#

FRL20.f <- forecast(m <-l(FRL20,FRL20.data),conditioning.inputs=FR_AUST.fx)

shockDecomposition(m)

tfplot(FRL20.f)