# Cut-and-Paste Code Below into Window Above and Run

#

# US_E20 Model (Growth-IMM), (FINN-LU), (IMM-FED)

#

# Measurement Matrix 

#     L.US.E.    L.U. GNP.US. GNP.C. GNP.I. GNP.X.  GNP.G. P.US.TBILL.

#[1,]  0.2193  0.0343 0.21805 0.2201 0.1707 0.1955  0.1404       0.195

#[2,] -0.0174 -0.2720 0.00525 0.0650 0.2130 0.1599 -0.0942      -0.139

#[3,]  0.0092 -0.4216 0.14585 0.0515 0.0263 0.0649  0.2549      -0.243

#     P.CPAPER. P.FED.FUNDS.  P.CPI.  P.GDP. P.SP500. V.NYSE.

#[1,]   -0.1338       0.0264  0.2070  0.2100   0.1479   0.102

#[2,]    0.3703       0.2991  0.1616  0.1439   0.0171   0.107

#[3,]   -0.0333      -0.3847 -0.0685 -0.0167  -0.0910  -0.331

#     P.S.P.DPR. P.S.P.EPR. Q.H.Starts.   K.US.      M1      M2 P.WPI.

#[1,]      0.194     0.0765       0.141  0.2065  0.2074  0.2157 0.1733

#[2,]     -0.195     0.3260       0.198 -0.0654 -0.0353 -0.0216 0.2417

#[3,]     -0.144    -0.0340       0.124 -0.2174  0.2181  0.1492 0.0686

#        Q.A.    Q.I.    O.B. P.FUELS. P.W.AG. P.W.MFG. Q.OIL.   N.US.

#[1,]  0.2131  0.2041 -0.0773   0.1561  0.2040   0.2217  0.215  0.2122

#[2,] -0.0695 -0.0759  0.3980   0.2611  0.0225   0.0201 -0.126 -0.0846

#[3,]  0.0571  0.1577 -0.1691  -0.0666  0.2419   0.0734 -0.021 -0.1495

#     IMM.US.   U.US.

#[1,]  -0.151  0.2107

#[2,]   0.143 -0.0888

#[3,]   0.248 -0.1705

#

# Fraction of Variance 

# [1] 0.637 0.774 0.855 0.904 0.937 0.958 0.970 0.978 0.984 0.990 0.993

#[12] 0.996 0.997 0.998 0.998 0.999 0.999 1.000 1.000 1.000 1.000 1.000

#[23] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

#

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

require(dse)

require(matlab)

f <- matrix( c(  1.0276420120, -0.04037731, -0.09367185, 0.324320408,

                      -0.0008705484,  0.95523103, -0.18831136, 0.059323285,

                      -0.0038330655,  0.10040247,  0.95814360, 0.009650457,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize the Model, Uncomment Next Line

# f[1,1] <- 0.95

#

h <- eye(3,4)

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

US_E20 <- SS(F=f,H=h,K=k,z0=c(0.324320408, 0.059323285, 0.009650457,  1.0000000000),

              output.names=c("US1","US2","US3"))

print(US_E20)

is.SS(US_E20)

stability(US_E20)

# tfplot(simulate(US_E20,sampleT=100))

US_E20.data <- simulate(US_E20,sampleT=100,noise=matrix(0,100,3),start=1900)

US_E20.f <- forecast(l(US_E20,US_E20.data),horizon=100)

tfplot(US_E20.f)

AIC(m <- l(US_E20,US_E20.data))

m1 <- SS(F=f[1:3,1:3,drop=FALSE],H=eye(3),Q=eye(3),R=eye(3),output.names=c("US1","US2","US3"))

stability(m1)

shockDecomposition(m1,shock=-1)