Hello, for a standard regression (including the panel case), normality is not a required assumption. (Unless you want to do "exact" inference in small samples.) So perhaps you don't need to do anything at all.

Having said that and with respect to your practical question, after initial estimation you could check for which observations the abs value of the residuals (which you can save from your initial estimate) exceeds, say, three times the standard error of the regression (which you can also save). Then you could either restrict the sample to where this is not the case, or you add a bunch of corresponding impulse/observation dummies to your model. Right now I don't have the time to be very specific about the details, sorry; maybe someone else can jump in. (The problem with excluding observations in the panel case is that very quickly the active dataset will lose the official panel status in gretl because of the gaps. So the dummy solution might be better.)

The "leverage" command would be useful, but unfortunately only works after the "ols" command, not after "panel". Maybe it could be enabled at least for fixed-effects estimation (in a future gretl version).

I'm not aware of any built-in functionality or contributed function package which automatically constructs outlier-related dummies yet, but maybe I'm missing something.

cheers

sven