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Re: st: use xtnbreg, fe or xtpoisson, fe vce(r)?

From   "Justina Fischer" <>
Subject   Re: st: use xtnbreg, fe or xtpoisson, fe vce(r)?
Date   Thu, 18 Oct 2012 09:11:12 +0200

Hi guys

there is a related discussion on the role of conditionality for the estimates comparing - xtnbreg, fe- with using  - nbreg - with manually added fixed effects:



-------- Original-Nachricht --------
> Datum: Thu, 18 Oct 2012 00:03:55 -0400
> Von: "JVerkuilen (Gmail)" <>
> An:
> Betreff: Re: st: use xtnbreg, fe or xtpoisson, fe vce(r)?

> On Wed, Oct 17, 2012 at 5:28 PM, Vestal, Alex - COB
> <> wrote:
> > Using unbalanced panel data, I am modeling a count DV with
> overdispersion (patent counts).  I recently submitted a study using xtnbreg and a
> reviewer commented: "the best practice is to use xtpoisson with robust SEs that
> resolves the usual reasons to favor xtnbreg."
> > I had been using xtnbreg because of its ability to handle
> overdispersion.  Is xtpoisson vce(robust) robust to overdispersion?  I'm looking for
> rationale as to which approach I should be using.  Any guidance would be
> greatly appreciated!
> >
> It's not obvious to me which one is best in this case. -xtnbreg- is
> using a parameter whereas vce(robust) is avoiding using a parameter.
> But I suspect the resulting inferences will end up being quite
> similar. So perhaps throw the reviewer a bone by doing what he wants
> and footnoting the other way, or vice versa.
> As to the issue of the sources of overdispersion, I've often
> encountered in practice the sad fact that any reasonable model will be
> overdispersed due to lack of reasonable covariates so often you simply
> have to fit what you have.
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