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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Why do logit model coefficients produce signs opposite to those obtained from OLS? |

Date |
Thu, 4 Feb 2010 07:26:56 -0800 (PST) |

--- On Thu, 4/2/10, Jitian Sheu wrote: > I am running a very very simple binary model, y=a+bX+e, > where y is a dummy variable. > Before performing -logit- command, I estimate the above > model by a traditional OLS, i.e. linear probability model > (regress y x1 x2...) > > I knew that OLS is not a good model for fitting this model. > I just want to get some direction from results obtained > from traditional OLS > > However, after I perform -regress- and -logit-, I found > signs of estimated coefficients from these two models are > not the same. > > I am just wondering whether this is "normal"? or I am doing > anything wrong? This is not normal. One possiblity I could imagine is that you specified the -or- option (or used -logistic-) and forgot to interpret coefficients less than 1 as negative effects. In both case you will get odds ratios, that is, the ratio by which the odds of "success" on y changes for a unit change in x. An odds ratio less than 1 thus means that a unit increase in x leads to a smaller odds, i.e. a negative effect, even though the numberical value of the odds ratio is positive. I wrote a small text about odds, odds ratios, and marginal effects (in the context of interaction effects), which is available here: <http://www.maartenbuis.nl/ wp/interactions.html> Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**References**:**st: Why do logit model coefficients produce signs opposite to those obtained from OLS?***From:*"Jitian Sheu" <jtsheu@mail.cgu.edu.tw>

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