[Limdep Nlogit List] Deviance for unconditional fixed effects negative binomial regression model

rjcal at u.washington.edu rjcal at u.washington.edu
Sat Aug 15 04:07:49 EST 2009

Dear LIMDEP users,

I'd like to follow up regarding the code I just posted for calculating adjusted standard errors for the unconditional fixed effects negative binomial regression, as I think I've got something wrong. As I understand it, the multiplier is supposed to always be greater than one, meaning that the deviance should always be greater than the degrees of freedom for the model. I think I'm making a mistake in going from Dr. Greene's notation in the LIMDEP manual to Dr. Allison and Dr. Waterman's notation in their Sociological Methodology article.

I think I'm wrong in one of these ways:
1. Allison and Waterman (2002, p. 253) discuss the overdispersion parameter lambda in introducing the NB-2 model (Cameron and Trivedi 1998), and allow lambda to vary by group (hence lambda_i). Elsewhere (see <http://books.google.com/books?id=23fNCTegnywC&pg=PA93&lpg=PA93&dq=Negative+binomial+NB2+mass+function&source=bl&ots=sERwjlX9S-&sig=s9kEg94qxoqcf93HBSbL1YEqSK4&hl=en&ei=yqGFSqOCGo_YsgO9kdyhBw&sa=X&oi=book_result&ct=result&resnum=4#v=onepage&q=Negative%20binomial%20NB2%20mass%20function&f=false>, pardon the long link), Dr. Allison specifies a model where the overdispersion parameter doesn't vary by group, and it doesn't vary by group in their simulation. So perhaps I should use the point estimate of the overdispersion parameter from the LIMDEP output in the deviance calculation for all observations.

2. I calculated (Allison and Waterman's) lambda = (Greene's) theta = exp(alpha) based on page E26-15 of the LIMDEP manual, but I think I mistakenly based that conversion the NB-1 parameterization and I should use theta = 1/alpha in the model (p. E24-16). But even making this correction, this doesn't entirely solve the problem -- deviance / degrees of freedom becomes greater than one for one of my dependent variables and about 0.8 for a second variable, so I'm still doing something wrong, unless the adjustment could actually revise the standard errors downwards (sounds contrary to the intent that Allison and Waterman had in their article).

3. I may be computing degrees of freedom for the model incorrectly. I'm using the number of observations actually used in the model (not associated with groups for which fixed effects are undefined), minus the number of parameters in the model (number of groups + number of parameters + 1 for the constant term). Or, N-(p+k+1).

4. My syntax could be wrong, of course -- particularly when calculating deviance -- although I've checked it a couple of times.

Again, Allison and Waterman's formula:

D = Sum_i{Sum_t{y_it*log(y_it/mu_it)-(y_it+lambda)log[(y_it + lambda)/(mu_it+lambda)]}}.

Thanks, all. Any suggestions? I'm going to try doing (1) and (2) above simultaneously and see if that works, but I'm skeptical.


Richard Callahan

/We need a federal commission for regulating probability distribution notation and an independent counsel for preserving degrees of freedom

Richard Callahan
Graduate Student, Department of Sociology
University of Washington
(206) 769-9812
rjcal at u.washington.edu

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