Dear Jack, dear Allin, Thanks for in depth explanation. You really helped a lot to put things in focus for me considering GIG and Gretl. My bad is that I did not check for normality immediate but I got stuck on VCV methods. You live you learn :)
Thanks once more, and I post comments when I gather up more info on restricting parameters. All the best, Davor On 20.7.2011. 12:22, Riccardo (Jack) Lucchetti wrote: > On Wed, 20 Jul 2011, Davor Horvatic wrote: > >> Dear Jack, >> >> I want first to thank you for detailed answer on the restriction of >> the GARCH parameters. I will look to dig some more details out if I can. > > I put some of that into the gig pdf doc; when CVS comes back up and > you can download it, your comments are welcome. > >> In this post I'll be as detailed as I can be. In attachment you will >> find >> time series used to reproduce numbers mentioned below. I'm wondering >> why is there discrepancy in std errors between GIG on one side and >> Eviews >> on the other. I.e. to be precise difference between Sandwich >> (default) and OPG or >> Hessian as VCV method. As you will see I get similar results for >> Eviews and GIG >> for all cases except for default Sandwich estimator. > > [...] > > Allin's script shows very clearly how things are done in gig. If you > ask me if I believe that's correct, my answer is yes. If you ask me if > everything else is wrong, my answer is no. As Allin said, there is a > number of asymptotically equivalent ways to obtain robust vcv > matrices; the trouble is, they may be very different from one another > in finite samples (and yes, 2746 observations may well be "not enough"). > > One possible difference is, as Allin said, the type of bread you use > in the sandwich: Hessian or information matrix? Another difference may > come from the fact that I used the delta method to compute the vcv for > the alternate parametrisation. Again, this is a quadratic form with > the Jacobian acting as the "bread" and the vcv for the original > parametrisation as the "ham". The choice of the type of ham should > make no difference asymptotically, but in finite samples it does. > > Moreover, your model seems to be misspecified in at least one respect: > if you run the following script fragment > > <hansl> > foo = gig_setup(ld_WIG, 3) > gig_estimate(&foo, 0) > series u = foo["stduhat"] > summary u > normtest u --all > > gig_set_dist(&foo, 1) > gig_estimate(&foo) > </hansl> > > you will see that assuming conditional normality is likely to be a > very bad idea; actually, if the "true" distribution is t with 5.9 > degrees of freedom I'm not even sure that the conditions for > asymptotic normality are satisfied (I'd need to check). In a setting > such as this, I'm not at all surprised that alternative choices for > robust inference may yield largely different results. > > > Riccardo (Jack) Lucchetti > Dipartimento di Economia > Università Politecnica delle Marche > > r.lucchetti(a)univpm.it > http://www.econ.univpm.it/lucchetti > > > _______________________________________________ > Gretl-users mailing list > Gretl-users(a)lists.wfu.edu > http://lists.wfu.edu/mailman/listinfo/gretl-users
Dear Jack, dear Allin, Thanks for in depth explanation. You really helped a lot to put things in focus for me considering GIG and Gretl. My bad is that I did not check for normality immediate but I got stuck on VCV methods. You live you learn :) Thanks once more, and I post comments when I gather up more info on restricting parameters. All the best, Davor On 20.7.2011. 12:22, Riccardo (Jack) Lucchetti wrote: On Wed, 20 Jul 2011, Davor Horvatic wrote: |