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Re: st: MIXLOGIT: marginal effects


From   Clive Nicholas <clivelists@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: MIXLOGIT: marginal effects
Date   Tue, 7 Feb 2012 07:50:10 +0000

Arne Risa Hole replied to Maarten Buis:

> Thanks Maarten, I take your point, but as Richard says there is
> nothing stopping you from calculating marginal effects at different
> values of the explanatory variables (although admittedly it's rarely
> done in practice). Also the LPM is fine as an alternative to binary
> logit/probit but what about multinomial models?

I'm coming in on this late, but this is to say two things. I tend to
agree with you over Maarten (whose posts I always read) about the
usefulness of marginal effects and how they should be used (although
Maarten is right that using such statistics as a single summary
measure undermine the whole point of fitting non-linear models).

However, both of you, IMVHO, are wrong, wrong, wrong about the linear
probability model. There is no justification for the use of this model
_at all_ when regressing a binary dependent variable on a set of
regressors. Pampel's (2000) excellent introduction on logistic
regression spent the first nine or so pages carefully explaining just
why it is inappropriate (imposing linearity on a nonlinear
relationship; predicting values out of range; nonadditivity; etc).
Since when was it in vogue to advocate its usage? I'm afraid that I
don't really understand this.

Simply put, it's logistic regression or, otherwise, don't bother yourself.

-- 
Clive Nicholas

[Please DO NOT mail me personally here, but at
<clivenicholas@hotmail.com>. Please respond to contributions I make in
a list thread here. Thanks!]

Pampel FC (2000) Logistic Regression: A Primer (Sage University Papers
Series on QASS, 07-132), Thousand Oaks, CA: Sage
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