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From |
jhilbe@aol.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: overdispersion and underdispersion in nbreg / glm models |

Date |
Thu, 18 Dec 2008 14:49:19 -0500 |

to catch your query.

are ones that have real overdispsersion.

greater than 1. Sometimes it is substantially greater than 1.

Joseph Hilbe ============================================ Date: Wed, 17 Dec 2008 10:36:00 +0000 From: "Ada Ma" <heu034@googlemail.com> Subject: st: overdispersion and underdispersion in nbreg / glm models Dear Statalisters, I'd been following Joseph Hilbe's book "Negative Binomial Regression" (2007) and using some of my own data to try out methods laid out in the book. The book suggested that one can look at the Pearson's dispersion output from the -glm- command to check if one's negative binomial model is affected by underdispersion or overdispersion. In the book it says that if one's model is affected by overdispersion, it could be caused by missing explanatory variable. But my model seems to be suggesting quite the opposite and I am not sure what to do. When I added an explanatory variable to the model the Pearson's stats went from being underdispersed to overdispersed. Both models are estimated using the -glm- command with the "family(nb XXX)" option specified, XXX being the alpha value taken from the -nbreg- command output. Although the AIC and BIC of the model with the additional variable looks better (lower), I really don't know what is worse. What I should do in order to resolve the dispersion problem and frankly speaking, are there other things that would tell me which model is better? Shall I bootstrap and jacknife??? All suggestions welcomed. Regards, Ada - -- Ada Ma Research Fellow Health Economics Research Unit University of Aberdeen, UK. http://www.abdn.ac.uk/heru/ Tel: +44 (0) 1224 553863 Fax: +44 (0) 1224 550926 * * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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