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.

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.

I propose naming this approach the Jeffit estimator.

"We used Jeffit to estimate the average partial effects and their .95 CIs."

"We compare our main results with those obtained using Jeffit."

"We compare our main results with those obtained using Jeffit."

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…

statalist.org/forums/forum/g…

This is probably a negligible concern when analyzing most "large" samples, but not necessarily so for "small" ones.

Earlier threads have considered the use of the –recast– option in @Stata graphics. Here's another.

The –twoway function– command in Stata permits nice visualizations of explicit functions y=f(x) over some continuous domain of x-values. E.g.

twoway function y=normal(x), range(-3 3)

twoway function y=normal(x), range(-3 3)

This can be helpful in...

— visualizing comparative features of different explicit functions

— visualizing theoretical vs. empirical results (e.g. goodness-of-fit)

— etc.

— visualizing comparative features of different explicit functions

— visualizing theoretical vs. empirical results (e.g. goodness-of-fit)

— etc.