WLM_IR

# Cut-and-paste code below into window above and Run

#

# World System -- Iran (1950-2000)

#

#

 #Measurement Matrix  (Growth), (LU+KOF) (KOF-EF-LU-EG)

 #

# Measurement Matrix 

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

#[1,]         0.3664             0.357        0.36749        0.36983     0.35974         0.0381 0.3639  0.359 0.273

#[2,]        -0.1003            -0.190       -0.00213        0.00266    -0.00723         0.9091 0.0550 -0.109 0.335

#[3,]        -0.0184            -0.217        0.10426       -0.09812    -0.16570        -0.3878 0.0648 -0.258 0.827

#

 #Fraction of Variance 

#[1] 0.801 0.924 0.979 0.995 0.998 0.999 1.000 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(   1.01282761, -0.02297798, 0.01177121, 0.174120663,

                       -0.01833407,  0.93276396, 0.14397290, 0.013356943,

                       -0.01894606, -0.04263376, 0.93461548, 0.005099341,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <- 0.99267234 ; f[2,2] <- 0.91420196; f[3,3] <- 0.91601663

#

h <- eye(3,4)

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

IR_LM <- SS(F=f,H=h,K=k,z0=c(0.174120663, 0.013356943, 0.005099341,  1.0000000000),

              output.names=c("IR1","IR2","IR3"))

print(IR_LM)

is.SS(IR_LM)

stability(IR_LM)

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

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

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

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

tfplot(IR_LM.f)

#

# World System with Iran Input # (Growth-Living Planet), (Living Planet - TEMP) (P.OIL + P.Wheat - EF - Earths)

#

# Measurement Matrix 

#           N    OIL      QA     GWP P.Wheat. P.Oil.   TEMP     CO2

#[1,]  0.2845  0.271  0.2821  0.2824    0.236  0.243  0.251  0.2837

#[2,] -0.0161  0.241  0.0985 -0.1413    0.411  0.066 -0.235 -0.0983

#[3,] -0.1112 -0.241 -0.0883  0.0213    0.461  0.712  0.190 -0.0201

#      Carbon TotalFootprint Earths WorldGlobal LivingPlanet   URBAN

#[1,]  0.2833          0.275  0.279      0.2819      -0.1839  0.2840

#[2,]  0.0925          0.182  0.148     -0.0926       0.7705 -0.0868

#[3,] -0.0955         -0.293 -0.231     -0.0943      -0.0576 -0.0707

#

# Fraction of Variance 

# [1] 0.874 0.941 0.973 0.986 0.995 0.998 0.999 0.999 1.000 1.000 1.000

#[12] 1.000 1.000 1.000

f <- matrix( c(   0.7170305,  0.07664195, -0.09079280,  0.6150190,

                          0.1268621,  0.74566835,  0.08042742, -0.1787823,

                          -0.1910752, -0.03247569,  0.97341666,  0.3096958,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[3,3] <- .90

#

g <- matrix( c(  0.4093634, -0.04138191, -0.10035658,

                        -0.2336921,  0.14439256,  0.29190548,

                          0.2876086,  0.03858389,  0.04058617,

                       0.000000000,  0.000000,  0.0000000)

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

h <- eye(3,4)

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

W_IR <- SS(F=f,H=h,K=k,G=g,z0=c(0.7568211, -0.2555046,  0.4115690,  1.0000000000),

              output.names=c("W1","W2","W3"),input.names=c("IR1","IR2","IR3"))

print(W_IR)

is.SS(W_IR)

stability(W_IR)

# W_IR.data <- simulate(W_IR,sampleT=250,start=1950,input=IR_LM.fx)

W_IR.data <- simulate(W_IR,sampleT=250,noise=matrix(0,250,3),input=IR_LM.fx)

m <- l(W_IR,W_IR.data)

tfplot(m)

AIC(m)

shockDecomposition(W_IR,shock=rep(-1,10))

#

#       Iran cut out of the World System for 30 years

#

x=IR_LM.fx

x[77:107,] <- 0       

f <- simulate(W_IR,sampleT=250,noise=matrix(0,250,3),input=x)

tfplot(f)