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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Binary Choice Model and fixed effects - interpreting the interaction effects? |

Date |
Mon, 2 Apr 2012 11:23:50 +0100 |

As my own earlier post indicated, people interested in -inteff- should download the later version from 4-3. This won't help your problem. On Mon, Apr 2, 2012 at 11:19 AM, Benjamin Niug <benjamin.niug@googlemail.com> wrote: > Sorry for the shortcomings of the description of my problem: > @Nick: It is SJ4-2: st0063; > @Maarten: Norton, Wang, Ai (2004), "Computing interaction effects and > standard errors in logit and probit models", The Stata Journal 4, > Number 2, pp. 154-167. > > Besides: > @Maarten: Thanks for the odds ratio hint. Do you know of other > solutions as well? > > Am 2. April 2012 12:10 schrieb Maarten Buis <maartenlbuis@gmail.com>: >> On Mon, Apr 2, 2012 at 11:57 AM, Benjamin Niug wrote: >>> I want to estimate a binary choice model accunting for time-invariant >>> fixed effects (I read I could use the -xtlogit- or -clogit- command). >>> >>> y_it = b_1*x_1_it*x_2_it+b_2*x_1_it + b_3*x_2_it >>> >>> However, I have included an interaction effect which I want to >>> interpret correctly - as pointed out by Ai and Norton (2004) this is >>> not trivial. They suggest to use a user written command called >>> -inteff-. This command works well if -logit- is used, however, it does >>> not work if -xtlogit- or -clogit- is used. >> >> Please note that just author-year references are not appreciated on >> this list. Please give the complete reference. This is discussed on >> the Statalist FAQ. The logic is that this is a multi-disciplinary >> list. Even if a citation is so famous within your >> (sub-(sub-)discipline that author-year suffices, this is likely not to >> be the case for the rest of the world. However, often many disciplines >> will have independently faced (and solved) the same problem, and they >> have something useful to say about the subject. >> >> You have a double problem here: a) interpreting marginal effects of >> interaction terms is hard, and b) interpreting marginal effects in >> multi-level/panel/fixed effects models is hard. So the combination of >> the two means that that is going to be very hard. >> >> However, the solution is simple: don't do marginal effects but >> interpret your coefficients in the natural metric of the model. In >> this case the odds of success. Odds, odds ratios and ratios of odds >> ratios have an undeserved reputation of being hard to interpret. You >> can see an example of how easy that is here in: >> >> M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in >> non-linear models", The Stata Journal, 10(2), pp. 305-308. >> * * 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/

**References**:**st: Binary Choice Model and fixed effects - interpreting the interaction effects?***From:*Benjamin Niug <benjamin.niug@googlemail.com>

**Re: st: Binary Choice Model and fixed effects - interpreting the interaction effects?***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Binary Choice Model and fixed effects - interpreting the interaction effects?***From:*Benjamin Niug <benjamin.niug@googlemail.com>

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