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Re: st: RE: poisson - bootstrapping or clustering?

From   Scott Cunningham <[email protected]>
To   [email protected]
Subject   Re: st: RE: poisson - bootstrapping or clustering?
Date   Tue, 12 Sep 2006 09:11:59 -0400


My main reason for sticking with the Poisson for the time being is that the negative binomial model is less robust to distributional misspecification than the POisson. The Poisson is consistent under the weak assumption that I've correctly the conditional mean - even if the data is not Poisson distributed. But from my understanding of Cameron and Trivedi (2005), chapter 20.2 and 20.4 (p. 666-677), the negative binomial will be inconsistent even if the conditional mean is correctly specified (except for a special case of the NB2 model).

Also, I'm using a fixed effects, and my understanding again is that the Poisson fixed effects has desirable characteristics - like not suffering from incidental parameter bias and not needing to specify any specific distribution for the unobserved random variable. Whereas with the negative binomial, I think I have to buy off on certain assumptions about the unobservable - that it is gamma distributed. I'm not an expert on count data and count models, though.

Insofar as I can correct for the overdispersion problem within the poisson mle, I wanted to do so. Historically, I've used bootstrapping, but it's been a while since I ran those models, and I can't figure out why now the model fails so often. I'm using numerous indicator variables - a six year panel with state fixed effects (51 total when you include District of Columbia), year fixed effects (6 years of data), a race and race interacted with a continuous variable. There are 2282 observations per year in the sample - a total of 13692 observations. I don't know if this is what is causing the problem or not. When I've successfully used bootstrapping to correct for overdispersion, I was not using a race dummy, plus only 700 or so observations with a shorter panel. Maybe that is part of the problem.


I will experiment with different seeds as you suggest.


On Sep 12, 2006, at 8:20 AM, Carter Rees wrote:

I missed the previous post you are referring to but why not use - xtnbreg- if
you know you have overdispersion in your data?

Carter Rees
School of Criminal Justice
University at Albany, SUNY
[email protected]

-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Scott Cunningham
Sent: Tuesday, September 12, 2006 8:13 AM
To: [email protected]
Subject: st: poisson - bootstrapping or clustering?

I'm estimating a model of sexual partners using -xtpoisson- and -
poisson-. The data suffers from overdispersion, and so I'm trying to
correct for that using bootstrapping within -xtpoisson-. But as I
posted the other day, I'm having trouble recovering the marginal
effects in post-estimation. I have a memory of someone telling me
that the cluster() option within -poisson- can correct for
overdispersion. Does anyone with experience in count data have
recommendations? This is micro-level data from the National
Longitudinal Survey of Youth (1997). The problem with -mfx, dydx-
appears to be that bootstrapping within -xtpoisson- had numerous
failures in calculating the standard errors. That's at least what I
think is going wrong.
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