Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: Re: st: MIXLOGIT: marginal effects


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Re: st: MIXLOGIT: marginal effects
Date   Thu, 9 Feb 2012 09:51:09 +0100

On Thu, Feb 9, 2012 at 3:08 AM, Nick Cox wrote:
> I can readily believe in Kit's colleague's counterexample without even
> seeing it. But if sometimes being quite the wrong model to fit is a
> fatal indictment, then nothing goes.
>
> I was responding to Clive's statement "There is no justification for
> the use of this model
> _at all_ when regressing a binary dependent variable on a set of
> regressors." I think that is too extreme. I can't readily imagine many
> situations in which I would prefer a linear probability model to a
> logit model, but I still think it's too extreme.

One situation I can imagine where the linear probability model might
be useful and unproblematic is what econometricians call a difference
in difference design: You have two groups --- one experienced the
treatment and one did not --- and two measurements --- one before the
treatment and one after. The change in the outcome within the control
group is thought to represent the change that would have occurred if
the treatment  was not administered and the effect of the treatment is
the change that occurred on top of that "natural" change. So it is
just a regression with two indicator variables and their interaction,
and the interaction effect is interpreted as the effect of the
treatment. There are many ways in which this can go wrong (e.g. where
did the control group come from?) and the results can easily be
over-interpreted, but I can still imagine many situations where a
difference in difference model is a meaningful and useful strategy.
Note that this is a fully saturated model, and as I showed earlier
<http://www.stata.com/statalist/archive/2012-02/msg00351.html>, a
logit or linear probability model give exactly the same predictions
and both give valid inference. So the choice between the two is just a
matter taste.

No model works well in all situations, it is just a matter of having a
large enough toolkit so you can choose the right model for the job at
hand.

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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index