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| From | Jeffrey Wooldridge <jmwooldridge60@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Poisson and marginal effects |
| Date | Sun, 13 May 2012 18:41:13 -0400 |
In Poisson regression the average partial effect of a continuous variable is just the sample average of y times the coefficient. Naturally, it is harder for a discrete change. There is makes sense to obtain the predicted value at the two different settings of the explanatory variable, with other variables evaluated at their observed values, and average the difference. The reason this is not regularly done in Poisson regression is that the coefficients have interpretations as percentage changes. On Sun, May 13, 2012 at 5:29 PM, Jessie C <jessiecoh@gmail.com> wrote: > If I am understanding Poisson regression correctly, its interpretation > is based on evaluating the coefficients at a certain data point like > probit. In probit, there is a marginal effects model, mfx, is that > right? That evaluates the probit results at the mean of the data. Is > there an analogue for poisson regression? > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/