On Thu, Jul 6, 2017 at 2:54 AM, Allin Cottrell <cottrell@wfu.edu> wrote:

On Thu, 6 Jul 2017, Periklis Gogas wrote:

On Fri, Jun 30, 2017 at 6:17 PM, Allin Cottrell <cottrell@wfu.edu> 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
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