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Re: st: Underdispersion in count data from a survery


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Underdispersion in count data from a survery
Date   Thu, 7 Oct 2010 19:15:50 -0400

--
You can use the -glm- command in Stata 9 with survey data (link(log)
family(poisson)) and follow it by -suest- to get valid standard
errors. See http://www.stata.com/statalist/archive/2007-06/msg01103.html.
  You could also run -svy reg- on the log of (count +1).
Under-dispersion is a concern for standard errors when your
inferences are likelihood based. The survey-based commands will base
standard errors on the sample design.

The Poisson or regression  models for predicting or estimating  means
might be useful even if the data are not Poisson distributed. You
still have to check model diagnostics. See:
http://www.stata.com/meeting/dcconf09/dc09_valliant.pdf  for some
ideas.

Steve



On Thu, Oct 7, 2010 at 5:46 PM, Laurie Molina <molinalaurie@gmail.com> wrote:
> Hello all,
>
> I have count data from a survey which comes from a complex survey
> design (stratification, clusters, two stages, and probabilistic
> design).
>
> The mean is 1.89 and the variance is 1.14.
>
> Fallowing "Essentials of count data regression" by Cameron and Trivedi
> (1999)To test for underdispersion i ran the following auxiliary
> regression:
>
> ((yi - muhati)^2 - yi)/muhati= alpha*muhati + ui
>
> where yi is the i observation of  original dependent count variable,
> muhati is the i observation of the fitted values resulting from the
> poisson coefficients estimated, ui is the error term, and the
> objective is to test alpha= 0 Vs alpha<0.
>
> Whith a p-value of 1, the hypothesis of alpha < 0 cannot be rejected.
>
> So my question is... What now? My data is underdispersed so the
> standard errors estimated when i run the Poisson regression are not
> correct (even when the estimates of my coefficents are consistent).
>
> Is there any solution for this problem in Stata 9, that can be used
> with survey data?
>
> Thank you all very much in advance.
>
> Laurie
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