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/
