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From |
Ori Katz <[email protected]> |

To |
[email protected] |

Subject |
Re: st: marginal effects of interaction variables after clogit |

Date |
Tue, 27 Mar 2012 11:47:42 +0200 |

Thanks, this is very helpful. Ori On Mon, Mar 26, 2012 at 5:42 PM, Maarten Buis <[email protected]> wrote: > > On Mon, Mar 26, 2012 at 4:51 PM, Ori Katz wrote: > > clogit choice1 c.m_inc12_jman i(1 > > 3/8)bn.ethnic_origin_ussr#c.m_inc12_jman, or group(id) > > > > you can see that I omit ethnic origin 2 (there are 8 categories). > > the easy way to do so is to type -ib2.ethnic_origin- instead of -i(1 > 3/8)bn.ethnic_origin-. > > > after the regression I try to run marginal effects, by using the > > command: > > margins, dydx(*) predict(xb) > > This not meaningfull, it just shows the "effect" on the linear > predictor, which is a beast without substantive interpretation in a > -clogit-. > > > I run into two problems in the results from the margins command: > > > > 1. the results table include m_inc12_jman and ethnic_origin_ussr, but > > not the interaction between them. > > Marginal effects of interaction terms in non-linear models are hard > and Stata won't calculate them. My take on that is that marginal > effects get so tricky with interaction terms that they are no longer > useful. Instead you should learn how to interpret (and explain to your > audience) the interaction in the natural metric of the model. > > In case of a -clogit- they are odds ratios and ratios of odds ratios. > These have an unjust (or at least exaggerated) reputation of being > hard to interpret, but they are actually quite easy: see for example: > M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in > non-linear models", The Stata Journal, 10(2), pp. 305-308. > > Alternatively, you can start with choosing the metric of interest as > linear additive on the probability, which is typically what one ends > up with when reporting marginal effects, and look for a model in which > that is the natural metric. This is just the linear probability model. > > I would typically prefer a logit over a linear probability model and > interpret my results in terms of odds, odds ratios, and ratios of odds > ratios. However, I would consider a linear probability more "honest" > than fitting a logit and report only one (average) marginal effect per > variable. The latter gives your audience only one additive effect per > variable, which is just a straight line. So is in essence marginal > effects used like that are just a linear model fitted on top of a > non-linear model. If you wanted a linear model, than you should have > estimated one to begin with and openly suffer all the consequences. > > > 2. Stata omits ethnic origin 1 and 2 from the results, but I want to > > omit only 2. > > That has to do with the weird way you specified the base-value: -i(1 > 3/8)bn.ethnic_origin-. With that you said -bn-, meaning there is no > base value. You manually override it by specifying all values you want > to include, but the -bn- part is dominant so Stata thinks it has to > exclude another value. You can (and should) prevent this problem by > just specifying -ib2.ethnic_origin-. > > 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/

**References**:**st: marginal effects of interaction variables after clogit***From:*Ori Katz <[email protected]>

**Re: st: marginal effects of interaction variables after clogit***From:*Maarten Buis <[email protected]>

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