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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
RE: st: dprobit and lincom |

Date |
Fri, 22 Jan 2010 02:53:39 -0800 (PST) |

--- On Thu, 21/1/10, Melanee Thomas wrote: > what's the appropriate syntax to use to get an > interpretable value from lincom after using dprobit using > mfx? I'm assuming such syntax exists and that others have > used it successfully..... It sounds like you want to interpret an interaction effect in a probit model, and you want to interpret the effects as marginal effects. That is actually a surprisingly complicated problem, see for example the article by Edward Norton and collegues (2004). A simple alternative is to use -logit- rather than -probit- and interpret the odds ratios, as is discussed here: <http://www.maartenbuis.nl/wp/interactions.html>. If you want to stick to marginal effects and -probit- you can compute those as in the example below, or you can take a look the -inteff- command by Edward Norton and collegues (see the reference at the bottom of this post, and -findit inteff-). *----------- begin example ----------------------- sysuse auto, clear gen bad = rep78 <= 3 if rep78 < . gen mpgXbad = mpg*bad probit foreign mpg weight bad mpgXbad sum mpg if e(sample), meanonly local xb = _b[_cons] + _b[mpg]*r(mean) sum weight if e(sample), meanonly local xb = `xb'+_b[weight]*r(mean) nlcom (good: normalden(`xb')*_b[mpg]) /// (bad: normalden(`xb' + _b[bad])*(_b[mpg]+_b[mpgXbad])) *-------------- end example -------------------- --- Austin Nichols wrote: > You should really not use -mfx- or -dprobit- at all, as the marginal > effect at the mean is not informative for most purposes I think that is a bit strong. When it comes to interpreting these interaction effects with marginal effects I would probably use both, as the average marginal effects not only include differences in effect but also differences in the distribution of the controll variables. The marginal effects at the means, by necesity control for this. So by looking at both I get an idea of how much is due differences in effects and how much is due to differences in distributions of the controll variables. Hope this helps, Maarten Edward C. Norton, Hua Wang, and Chunrong Ai (2004) Computing interaction effects and standard errors in logit and probit models. The Stata Journal, 4(2): 154--167. http://www.stata-journal.com/article.html?article=st0063 -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**Follow-Ups**:**Re: st: dprobit and lincom***From:*Austin Nichols <[email protected]>

**References**:**RE: st: dprobit and lincom***From:*Melanee Thomas <[email protected]>

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