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Re: Re: st: MIXLOGIT: marginal effects

From   Nick Cox <>
Subject   Re: Re: st: MIXLOGIT: marginal effects
Date   Thu, 9 Feb 2012 02:08:48 +0000

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.


On Wed, Feb 8, 2012 at 8:22 PM, Christopher Baum <> wrote:
> <>
> 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.
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