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

From   Benjamin Niug <>
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.

@Maarten: Thanks for the odds ratio hint. Do you know of other
solutions as well?

Am 2. April 2012 12:10 schrieb Maarten Buis <>:
> 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
> --------------------------
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