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require("dse")
data("eg1.DSE.data", package = "dse")
data("eg1.DSE.data.diff", package = "dse")
cat("truncate sample to 240 periods.\n")
eg1.DSE.data.diff.trunc <- TSdata(input= inputData(eg1.DSE.data.diff)[1:240,, drop=F],
output=outputData(eg1.DSE.data.diff)[1:240,])
seriesNames(eg1.DSE.data.diff.trunc) <- seriesNames(eg1.DSE.data.diff)
cat("estimates a VAR model using the truncated sample.\n")
V.1 <- estVARXar(eg1.DSE.data.diff.trunc)
cat("calculate the likelihood, one step ahead predictions, etc.\n")
l.V.1 <-l(V.1, eg1.DSE.data.diff.trunc)
cat("Likelihood and components for VAR model\n")
# (with a breakdown for the 3 terms of the likelihood function).
print(l.V.1$estimates$like, digits=16)
#cat("Likelihood and components for VAR model\n")
#l.V.1$estimates$like # also prints the value but not as many digits.
cat("likelihood, one step ahead predictions, etc., based on the full sample.\n")
o.V.1 <-l(V.1, eg1.DSE.data.diff)
cat("convert the VAR model to a state space model balanced by Mittnik's technique.\n")
SS.V.1 <- toSS(V.1)
cat("likelihood, one step ahead predictions, etc., based on truncated sample.\n")
l.SS.V.1 <-l(SS.V.1, eg1.DSE.data.diff.trunc)
cat("Likelihood and components for state space model\n")
print(l.SS.V.1$estimates$like,digits=16)
cat("Maximum difference in one-step predictions of VAR and state space model ")
# calculate the difference of the absolute values of the predictions of
# the two models.
cat(max(abs(l.V.1$estimates$pred - l.SS.V.1$estimates$pred)))
cat("\n")
cat("Exhibit 2. Mittnik reduction from VAR model: \n")
M5.SS.V.1 <- MittnikReduction(SS.V.1, data=eg1.DSE.data.diff, criterion="taic")
cat(paste(
" If criterion is not specified the program prompts for a state dimension\n",
" and returns that model. Results is put in the variable M5.SS.V.1."))
cat("Exhibit 3. Mittnik estimation lag=3: \n")
M12.shift3 <- estSSMittnik(eg1.DSE.data.diff.trunc, max.lag=3, n=12)
M12.shift3 <- MittnikReduction(M12.shift3, data=eg1.DSE.data.diff.trunc, criterion="taic")
cat("Exhibit 4. Mittnik estimation lag=4: \n")
M12.shift4 <- estSSMittnik(eg1.DSE.data.diff.trunc,max.lag=4, n=15)
M12.shift4 <- MittnikReduction(M12.shift4, data=eg1.DSE.data.diff.trunc, criterion="taic")
cat(paste(
"Plot cpi in year over year % change.\n",
"Prediction is relative to previous month's actual (eg1.DSE.data)\n",
"and % change is relative to actual.\n",
"240 is the starting point for plotting.\n",
"base is the start value of the undif, un logged series.\n"))
i <- 3 # cpi is the third variable
base <- eg1.DSE.data$output[1, i]
pred <- o.V.1$estimates$pred[, i]
y <- o.V.1$data$output[, i]
y <- cumsum(c(log(base), y))
pred <- c(log(base), pred) # cumsum using pred relative to actual
pred[2:length(pred)] <- pred[2:length(pred)] + y[1:(length(pred) - 1)]
pred <- exp(pred)
y <- exp(y)
pred <- 100 * ((pred[13:length(pred)] - y[1:(length(y) - 12)])/y[1:(
length(y) - 12)])
y <- 100 * ((y[13:length(y)] - y[1:(length(y) - 12)])/y[1:(length(y) -
12)])
tfplot(tfwindow(y, start=240),tfwindow(pred, start=240))
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