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


From   Muhammad Anees <anees@aneconomist.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: use xtnbreg, fe or xtpoisson, fe vce(r)?
Date   Thu, 18 Oct 2012 08:57:55 +0500

Hello,

Without confirming the causes of overdispersion, proceeding towards a
point may itself be not correct. Quoting from Hilbe, (2011) pp.142
(detailed reference above signature) indicates a few reasons of the
overdispersion:

Apparent overdispersion occurs when:
(a) the model omits important explanatory predictors;
(b) the data include outliers;
(c) the model fails to include a sufficient number of interaction terms;
(d) a predictor needs to be transformed to another scale;
(e) the assumed linear relationship between the response and the link function
and predictors is mistaken, i.e. the link is misspecified.

So what is the alternatives, yes it the option vce(robust) may take
into account many issues, but still there is a chance to have
overdispersion. Jo Hilbe again suggested me to to detect
overdispersion via a personal email discussion which I have already
shared on this list for the benefit of a few users and which can be
found from the search on archives (FAQ of Statalist also suggest to
search before) or directly
http://www.stata.com/statalist/archive/2012-01/msg00200.html

Recently Hilbe and James Hardin have provided alternative techniques
which uses Generalized Poisson Models and can be a better alternative
in to these cases.

I hope this helps a little to check the reasons and find a way for correction.

Best
Anees


On Thu, Oct 18, 2012 at 2:28 AM, Vestal, Alex - COB
<Alex.Vestal@bus.oregonstate.edu> 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!
>
> Best,
>
> Alex Vestal, Ph.D.
> Assistant Professor of Technology Management
> College of Business
> Oregon State University
>
>
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-- 

Best
---------------------------
Muhammad Anees
Assistant Professor/Programme Coordinator
COMSATS Institute of Information Technology
Attock 43600, Pakistan
http://www.aneconomist.com

*
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*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/


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