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Re: st: marginal effects of interaction variables after clogit

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.


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
> --------------------------
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