A few points:
1. If you take the log of the number of days then you have a non-count
dependent variable. How would you interpret the estimated coefficients in a
Poisson or negative binomial model?
2. For the Poisson MLE, valid inference requires equality of the conditional
mean and variance (equidispersion) - it does not require that the dependent
variable have a Poisson distribution.
3. The Poisson is still consistent if the count data are over-dispersed
though the t-stats will tend to be inflated. (See Cameron and Trivedi
"Regression analysis of count data" p 59-60)
4. -nbreg- provides a likelihood ratio test that alpha = 0.
Scott
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> statalist@hsphsun2.harvard.edu] On Behalf Of Hugh Colaco
> Sent: Thursday, March 23, 2006 9:12 AM
> To: statalist
> Subject: st: Count data regression
>
> My dependent variable is the number of days, a count variable which is
> censored from below at 2. The summary stats are below (see # 1). As
> you can see, the number of days ranges from 2 to 2426. Since the
> unconditional variance > mean, I assume that I should use a Neg
> binomial reg rather than a Poisson reg.
>
> However, if I take the log of the number of days, then the
> unconditional variance < mean (see # 2), so I could run a Poisson reg.
>
> Any thoughts on the above? Is there any issue if I first take the log
> and then run a poisson or neg binomial regression? Would it defeat the
> very purpose of the count regression? Anything else I need to
> consider?
>
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