|
Archive@NYU >
Stern School of Business >
Economics Working Papers >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26046
|
| Title: | Fixed and Random Effects Models for Count Data |
| Authors: | Greene, William |
| Keywords: | Poisson regression
Negative binomial
Panel data
Heterogeneity;
Lognormal;
Fixed effects
Random effects |
| Issue Date: | May-2007 |
| Series/Report no.: | EC-07-16 |
| Abstract: | The most familiar fixed effects (FE) and random effects (RE) panel data
treatments for count data were proposed by Hausman, Hall and Griliches
(HHG) (1984). The Poisson FE model is particularly simple and is one of
a small few known models in which the incidental parameters problem is,
in fact, not a problem. The same is not true of the negative binomial
(NB) model. Researchers are sometimes surprised to find that the HHG
formulation of the FENB model allows an overall constant a quirk that
has also been documented elsewhere. We resolve the source of the
ambiguity, and consider the difference between the HHG FENB model and a
‘true’ FENB model that appears in the familiar index
function form. The familiar RE Poisson model using a log gamma
heterogeneity term produces the NB model. The HHG RE NB model is also
unlike what might seem the natural application in which the
heterogeneity term appears as an additive common effect in the
conditional mean. We consider the lognormal model as an alternative RENB
model in which the common effect appears in a natural index function form. |
| URI: | http://hdl.handle.net/2451/26046 |
| Appears in Collections: | Economics Working Papers
|
All items in Faculty Digital Archive are protected by copyright, with all rights reserved.
|
