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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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. Nick On Wed, Feb 8, 2012 at 8:22 PM, Christopher Baum <kit.baum@bc.edu> 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. > * * 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/

**Follow-Ups**:**Re: st: MIXLOGIT: marginal effects***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: MIXLOGIT: marginal effects***From:*brendan.halpin@ul.ie (Brendan Halpin)

**Re: Re: st: MIXLOGIT: marginal effects***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**re: Re: st: MIXLOGIT: marginal effects***From:*Christopher Baum <kit.baum@bc.edu>

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