A next step would be to replace some/all of the –poisson– goodness-of-fit statistics with ones that might be more relevant for nonlinear conditional-mean estimation.
And of course I should have specified vce(robust)
E.g. (w/ apologies to Chris and Bill)...
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In light of yesterday's massive thread on Poisson regression I thought it perhaps appropriate to revisit an issue that arises sometimes with Poisson estimation in Stata.
This will be familiar to some of you but perhaps not to others.
The typical case is where there are ≥1 dummy RHS variables that are almost always 0 (or almost always 1).
The Poisson estimator requires solving the vector of equations x'(y-exp(x*b))=0. This solution requires in turn that none of the dummy x's can equal 1 *only* when y=0. Else x'y=0 and the algorithm is trying to find a value of b that makes exp(x*b)=0 which can't happen.
If you use @Stata to compute/estimate quantiles/percentiles there's a Statalist thread that may be of interest. (Spoiler: Different commands can yield different results—except for the median—so exercise care with tail-probability, IQR, etc. calculations.) statalist.org/forums/forum/g…
This is probably a negligible concern when analyzing most "large" samples, but not necessarily so for "small" ones.