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Re: st: MIXLOGIT: marginal effects


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: MIXLOGIT: marginal effects
Date   Mon, 6 Feb 2012 17:56:16 +0100

On Mon, Feb 6, 2012 at 3:03 PM, Arne Risa Hole wrote:
> I disagree when it comes to marginal effects: I personally find them
> much easier to interpret than odds-ratios. In the end the choice will
> depend on your discipline and personal preference.

My point is that it is fine if you prefer to think in terms of
differences in probabilities, but in that case just go for a linear
probability model. If you are only going to report marginal effects
than you will summarize the effect size with one additive coefficient,
which is just equivalent to a linear effect. By going through the
"non-linear model-marginal effects" route you are doing indirectly
what you can do directly with a linear probability model. Direct
arguments are more clearer than indirect arguments, so they should be
preferred.

Even if you are uncomfortable with a linear probability model,  the
"non-linear model-marginal effects" route is still not going to help.
The non-linear model will circumvent the linearity which is in such
cases a problem, but than you are undoing the very reason for choosing
a non-linear model by reporting only marginal effects.

In short, there are very few cases where I can think of a useful
application of marginal effects: either you should have estimated a
linear model in the first place rather than post-hoc "fixing" a
non-linear one or you are undoing the very non-linearity that was the
reason for estimating the non-linear model in the first place.

Hope this clarifies my point,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------
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