IN_LM_SC Model

#

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

#

# IN_LM_SC India Small Country Model

#

# AXJ Regional Input  Input (RE)

#

#

# AXJ Asia minus Japan (1950-2000)

#

 #Measurement Matrix   (Growth) (HOURS) (X-N)

 #

#          Q      N  XREAL

#[1,]  0.581  0.574  0.578

#[2,] -0.224  0.795 -0.564

#[3,]  0.782 -0.198 -0.590

#

# Fraction of Variance 

#[1] 0.986 1.000 1.000

#

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

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)

f <- matrix( c(   1.032483086, 0.0893329688,  7.3532892,  1.146422e-01,

                        -0.015620695, 0.9745223725, -6.5969415,  1.761040e-03,

                         0.000010184, 0.0007955634,  0.8679133, -7.008574e-05,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <- .98

#

h <- eye(3,4)

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

AXJ_LM <- SS(F=f,H=h,K=k,z0=c(1.146422e-01,  1.761040e-03, -7.008574e-05,  1.0000000000),

              output.names=c("AXJ1","AXJ2","AXJ3"))

print(AXJ_LM)

is.SS(AXJ_LM)

stability(AXJ_LM)

AXJ_LM.data <- simulate(AXJ_LM,sampleT=100,start=1950)

#AXJ_LM.data <- simulate(AXJ_LM,sampleT=100,noise=matrix(0,100,3),start=1950)

AXJ_LM.f <- forecast(l(AXJ_LM,AXJ_LM.data),horizon=150)

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

#

# IN_LM_SC Model

#

# Measurement Matrix 

#         Q      N

#[1,] 0.707  0.707

#[2,] 0.707 -0.707

#

# Fraction of Variance 

#[1] 1 1

#

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

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)

f <- matrix( c(  -2.605049e-13, 3.270199e-13, -2.461127e-14,

                        -1.117852e-13 ,1.252353e-13, -1.038220e-14,

                        0.000000000,  0.00000000, 1.0000000000)

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

#       To Random Walk, uncomment next line

#        f <- eye(3)

#

g <- matrix( c(  0.81867254,  0.2053109,  3.308165,

                        0.00223055, -0.6147680, -1.405612,

                       0.000000000,  0.000000,  0.0000000)

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

h <- eye(2,3)

k <- (f[,1:2,drop=FALSE])

IN_LM <- SS(F=f,H=h,K=k,G=g,z0=c(-1.6951995,  0.2577112,  1.0000000000),

              output.names=c("IN1","IN2"))

print(IN_LM)

is.SS(IN_LM)

stability(IN_LM)

tfplot(outputData(IN_LM.data <- simulate(IN_LM,sampleT=250,start=1950,input=AXJ_LM.fx)))

#IN_LM.data <- simulate(IN_LM,sampleT=250,noise=matrix(0,250,2))

m <- l(IN_LM,IN_LM.data)

AIC(m)

shockDecomposition(toSSChol(m))