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re: Re: st: MIXLOGIT: marginal effects


From   Christopher Baum <kit.baum@bc.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   re: Re: st: MIXLOGIT: marginal effects
Date   Wed, 8 Feb 2012 15:22:00 -0500

<>
Clive said

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.


I don't understand it either, and I agree wholeheartedly with the sentiment. The undergrad textbook from which I teach Econometrics,
Jeff Wooldridge's excellent book, has a section on the LPM; I skip it and tell students to stay away from it. Unfortunately, much of the 
buzz about the usefulness of the LPM has arisen from the otherwise-excellent book by Angrist and Pischke, Mostly Harmless
Econometrics, in which they make strong arguments for the use of the LPM as an alternative to logistic regression.

One of my econometrician colleagues has come up with a nifty example of how, in a very simple context involving a LPM with
a binary treatment indicator, the LPM gets the sign wrong! A logistic regression, even though it fails to deal with any further issues
regarding the treatment variable, gets the right sign.

Kit

Kit Baum   |   Boston College Economics and DIW Berlin   |   http://ideas.repec.org/e/pba1.html
An Introduction to Stata Programming   |   http://www.stata-press.com/books/isp.html
An Introduction to Modern Econometrics Using Stata   |   http://www.stata-press.com/books/imeus.html


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