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Re: st: nbreg with fixed effect vs xtnbreg,fe
Muhammad Anees <email@example.com>
Re: st: nbreg with fixed effect vs xtnbreg,fe
Wed, 8 Feb 2012 10:50:07 +0500
Also the abstract in online from Guimarães, P (2008) is
In this paper I show that the conditional fixed effects negative
binomial model for count panel data does not control for individual
fixed effects unless a very specific set of assumptions are met. I
also propose a score test to verify whether these assumptions are met.
The full reference for the paper is
Guimarães, P., (2008), The fixed effects negative binomial model
revisited, Economics Letters, 99, pp63–66
It, thus, indicates to take care when to choose the fixed effects
model while using Negative Binomial Regressions.
On Wed, Feb 8, 2012 at 10:33 AM, Richard Williams
> At 08:52 PM 2/7/2012, Shikha Sinha wrote:
>> Hi all,
>> I emailed my query to tech support at Stata corp and below is the
>> Typically for a fixed effects negative binomial model, you would want to
>> the -xtnbreg, fe- command. -xtnbreg, fe- is fitting a conditional fixed
>> effects model. When you include panel dummies in -nbreg- command, you are
>> fitting an unconditional fixed effects model. For nonlinear models such
>> the negative binomial model, the unconditional fixed effects estimator
>> produces inconsistent estimates. This is caused by the incidental
>> problem. See the following references for theoretical aspects on the
>> incidental parameters problem:
>> Greene, William H. "Econometric Analysis". Prentice Hall.
>> Seventh Edition, page 413.
>> Baltagi, Badi "Econometric Analysis of Panel Data".
>> 4th. Edition. John Wiley and Sons LTD.
>> Section 11.1 (pages 237-8).
> Here is the abstract for the Allison & Waterman paper I mentioned before:
> "This paper demonstrates that the conditional negative binomial model for
> panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true
> fixed-effects method. This method which has been implemented in both Stata
> and LIMDEP-does not in fact control for all stable covariates. Three
> alternative methods are explored. A negative multinomial model yields the
> same estimator as the conditional Poisson estimator and hence does not
> provide any additional leverage for dealing with overdispersion. On the
> other hand, a simulation study yields good results from applying an
> unconditional negative binomial regression estimator with dummy variables to
> represent the fixed effects.
> There is no evidence for any incidental parameters bias in the coefficients,
> and downward bias in the standard error estimates can be easily and
> effectively corrected using the deviance statistic. Finally, an approximate
> conditional method is found to perform at about the same level as the
> unconditional estimator."
> And, from the conclusion:
> "The negative binomial model of Hausman, Hall, and Griliches (1984) and its
> associated conditional likelihood estimator does not accomplish what is
> usually desired in a fixed-effects method, the control of all stable
> covariates. That is because the model is based on a regression decomposition
> of the overdispersion parameter rather than the usual regression
> decomposition of the mean. Symptomatic of the problem is that programs that
> implement the conditional estimator have no difficulty estimating an
> intercept or coefficients for time-invariant covariates."
> The empirical examples Allison provides (p. 64 of his book), for which he
> says "None of this makes sense for a true fixed effects estimator" seem
> pretty compelling to me, but I remain open to persuasion or correction.
> Allison, Paul D. and Richard Waterman (2002) "Fixed effects negative
> binomial regression models." In Ross M. Stolzenberg (ed.), Sociological
> Methodology 2002. Oxford: Basil Blackwell.
> Also, Paul Allison, "Fixed effects regression models", Sage, 2009.
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
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