Re: [Gretl-users] GARCH results differences

On Thu, 6 Jul 2017, Periklis Gogas wrote: On Fri, Jun 30, 2017 at 6:17 PM, Allin Cottrell <cottrell(a)wfu.edu&gt; wrote: > > On Fri, 30 Jun 2017, Periklis Gogas wrote: >> >> I run an AR(10)-GARCH(2,2) model just for an example using the included >>> data file djclose.gdt >>> I run the following: >>> >>> *Model 1:* >>> Model>Time Series>GARCH Variants and got this: >>> [image: Inline image 1] >>> >>> *Model 2:* >>> Model>Time Series>GARCH and got this: >>> [image: Inline image 2] >>> >>> Why do I get so different results on the same data and model? The >>> results are very different in both the mean equation and the GARCH >>> part. They are both an AR(10)-GARCH(2,2) in the logs. >>> >> >> I wouldn't say the results are very different: they're qualitatively >> similar and both sets suggest an over-parameterized/misspecified model. >> > > First of all thank you very much for the response! > > I selected these models ​jut to show this difference they were not the > product of any model selection procedure. > OK. Gig finds a slightly higher log-likelihood; >> > > ​What is "gig"? > "gig" is "Garch in gretl", the addon package which supplies the "GARCH variants" menu item. the built-in garch command warns that the norm of the gradient at >> "convergence" is too big. >> > ​Where can I see this?​ > With current gretl (the last release is 2017b, from May of this year), the warning is printed under the GARCH estimation results. Ah, but I see the message is not shown in the GUI model window, only when the garch command is executed via script or in the gretl console -- that's something we should fix. This script will show the message: open djclose.gdt logs djclose garch 2 2 ; l_djclose 0 l_djclose(-1 to -10) shows: "Warning: norm of gradient = 4.84663". The norm of the gradient should be much smaller than that when convergence on the MLE has truly been achieved. Apparently there is not a well-defined MLE. >> >> ​Thank you very much and sorry for the possibly stupid questions.​ > They're not stupid questions, but complex nonlinear estimators are not guaranteed to work well when the model is misspecified and the MLE is either non-existent or hard to find. Allin Cottrell _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users