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From |
Benjamin Niug <benjamin.niug@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Mon, 2 Apr 2012 12:19:32 +0200 |

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. > > Hope this helps, > Maarten > > -------------------------- > 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/ * * 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: 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:*Nick Cox <njcoxstata@gmail.com>

**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>

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