*
* Example 10.3.1 from pp 353-356
*
open data e1032.dat
data(org=columns) 1 464 dowj
*
* The GARCH instruction, by default, will estimate the mean of the process along
* with the variance parameters. This will give a different estimate of the mean
* because it "downweights" the observations with a high variance.
*
garch(p=1,q=1,hseries=h) / dowj
*
* Do the stacked graph of the returns with the estimated variances
*
spgraph(vfields=2,footer="Figure 10.10 Returns and GARCH Estimates of Variance")
graph
# dowj
graph
# h
spgraph(done)
*
* If the data are already mean zero (by, for instance, doing a DIFF(CENTER)
* instruction), you add the NOMEAN option to the GARCH instruction.
*
diff(center) dowj / cdow
garch(p=1,q=1,nomean) / cdow
*
* This estimates the GARCH model with a t distribution
*
garch(p=1,q=1,distrib=t,resids=u,hseries=h) / dowj
*
* Compute the standardized residuals
*
set stdu = u/sqrt(h)
*
* Test them for independence
*
@bdindtests(number=20) stdu