RU_LM Model

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

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# Russia (RU) Model (1950-2000)

#

#

# Measurement Matrix (Growth), (L+Q-X), (Q-L-X)

#         Q      N  XREAL      X      L

#[1,] 0.426  0.499  0.481  0.436  0.384

#[2,] 0.501 -0.148 -0.322 -0.502  0.609

#[3,] 0.578  0.242  0.116 -0.380 -0.670

#

# Fraction of Variance 

#[1] 0.778 0.965 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( 0.965721491,  0.09701327, 0.02827611,  0.09664265,

                     -0.061814596,  1.09932370, 0.01687737, -0.05077295,

                     -0.005273331, -0.09700021, 1.03531507,  0.03912558,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment the following line

# f[1,1] <-  0.918391529; f[2,2] <- 1.04544590;      f[3,3] <- 0.98457433

#

h <- eye(3,4)

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

RU_LM <- SS(F=f,H=h,K=k,z0=c(0.09664265, -0.05077295,  0.03912558,  1.0000000000),

              output.names=c("RU1","RU2","RU3"))

print(RU_LM)

is.SS(RU_LM)

stability(RU_LM)

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

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

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

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

tfplot(RU_LM.f)

AIC(m <-  l(RU_LM,RU_LM.data))

RU_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.329380291,  0.032283718, -0.006625928,  1.0000000000),

              output.names=c("RU1","RU2","RU3"))

     shockDecomposition(RU_LMx)

#

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

# [1,]  0.95418  0.965721  0.977383    0.35    0.65   0.01082  0.004502  0.4162

# [2,] -0.07619 -0.061815 -0.052261    0.40    0.60   0.01115  1.032161 92.5910

# [3,] -0.01582 -0.005273  0.006368    0.51    0.49   0.01021  0.970595 95.0490

# [4,]  0.07158  0.097013  0.126753    0.37    0.63   0.02531  0.873117 34.5002

# [5,]  1.05286  1.099324  1.163530    0.32    0.68   0.05513 -0.123951 -2.2482

# [6,] -0.14704 -0.097000 -0.062640    0.61    0.39   0.03802  1.064341 27.9974

# [7,] -0.04768  0.028276  0.098497    0.55    0.45   0.05614  0.934901 16.6530

# [8,] -0.07677  0.016877  0.112283    0.62    0.38   0.08108  0.930348 11.4743

# [9,]  0.97406  1.035315  1.111009    0.28    0.72   0.05432 -0.044889 -0.8263

#[10,]  0.07374  0.096643  0.118256    0.56    0.44   0.01938  0.866906 44.7228

#[11,] -0.08598 -0.050773 -0.024734    0.51    0.49   0.02829  1.020412 36.0723

#[12,]  0.02062  0.039126  0.061089    0.51    0.49   0.01827  0.922969 50.5248

#