Wednesday, March 08, 2006

Re: st: poisson

"robust specifies that the Huber/White/sandwich estimator of variance be used in place of the traditional calculation; see [U] 20.14 Obtaining robust variance estimates" which is not the same as estimating a negative binomial model, where the variance and mean of Y conditional on X are not given by the same functional form (unlike poisson models). That said, poisson models have the very nice feature of being consistent under *very* weak assumptions.

On the other hand, the fixed-effects poisson you have estimated suffers from the incidental parameters problem--you might try: . xi:i.year . xtpoisson depvar indepvar _Iyear*, fe i(id) cluster(id) robust . xtpoisson depvar indepvar _Iyear*, fe i(id) . xtnbreg depvar indepvar _Iyear*, fe i(id) and maybe you can calculate deviance statistics by hand to measure goodness-of-fit, though I'm not sure about that part. If someone else on the list has an idea on how to generalize the techniques (mentioned at -help poisson postestimation-), please chime in.

On 3/7/06, Scott Cunningham <> wrote: > I am estimating a Poisson with fixed effects model using: > > xi:poisson depvar indepvar i.year, robust > > I use the "robust" to correct the standard errors because of > overdispersion. > > Moments ago, I was looking at various poisson postestimation > commands. I ran: > > estat gof > > The chi-squared result shows the poisson is not appropriate. But > reading on, I couldn't be sure whether correcting the standard errors > for overdispersion was the correct response to the problem the > postestimation command was revealing. If the chi-squared result from > a goodness of fit result is as large as I said, but I've corrected > the standard errors for overdispersion, then should I be skeptical of > using Poisson in this situation? If so, can someone help me see the > intuition? Thanks.

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