World1 Model

#

# World1 Forrester (Cut and paste code into window above and Run (Cmd-Enter)

#

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

require(dse)

require(matlab)

#

# Measurement Matrix # (N + K + POL - NR - QL) (QL + N + K) (POL + QL - N - NR)

#           NR     QL      N      K    POL

#[1,] -0.46659 -0.399  0.440  0.464 0.4619

#[2,] -0.00684  0.841  0.494  0.220 0.0279

#[3,] -0.36164  0.354 -0.596 -0.105 0.6143

#

# Fraction of Variance 

# 0.909 0.984 1.000 1.000 1.000

#

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

require(dse)

require(matlab)

f <- matrix( c(0,    0,    0, -0.93989620,  0.09577918, -0.03382348,  0.000000000,

                     0 ,   0,    0, -0.06989283, -1.13162828,  0.07219068,  0.000000000,

                     0 ,   0,    0, -0.08617730, -0.19537459, -0.90189005,  0.000000000,

                     1,    0,    0,  1.93850140, -0.09551917,  0.03364973,  0.002188603,

                     0,    1,    0,  0.07149041,  2.13136386, -0.07368290, -0.003111204,

                     0,    0,    1,  0.08816385,  0.19670294,  1.89730057, -0.003987851,

                     0,    0,    0,  0.00000000,  0.00000000,  0.00000000,  1.000000000

),byrow=TRUE,nrow=7,ncol=7)

h <- matrix(c(0,    0,    0,    1,    0,    0,    0,

                     0,    0,    0,    0,    1,    0,    0,

                     0,    0,    0,    0,    0,    1,    0

),byrow=TRUE,nrow=3,ncol=7)

k <- (f[1:7,3:5,drop=FALSE])

World1 <- SS(F=f,H=h,K=k,z0=c(00.000000000,  0.000000000,  0.000000000,  0.002188603, -0.003111204,

                                                    -0.003987851,  1.0000000000),

              output.names=c("W1","W2","W3"))

print(World1)

is.SS(World1)

stability(World1)

# tfplot(simulate(World1,sampleT=100))

World1.data <- simulate(World1,sampleT=100,noise=matrix(0,100,3),start=1900)

World1.f <- forecast(m <- l(World1,World1.data),horizon=150)

tfplot(World1.f)

shockDecomposition(m)