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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Fixed effects logit model |

Date |
Mon, 19 Jul 2010 13:50:11 +0000 (GMT) |

--- On Mon, 19/7/10, Marc Michelsen wrote: > I am estimating a logit model for a panel style data set. > In order to guarantee unbiased estimation, I have used company, > industry and/or offer year clusters (per Petersen, 2009). For > my linear regressions I have made positive experience with > fixed-effects models. Their application for binary outcome > models is not as straightforward because the models rely solely > on within-variance. > > more than 50% of my observations get lost in the regression > because of zero within variance. Is it consistent to show also > a fixed effects logit model beside standard logit models > clustered by the above mentioned characteristics. I would not do that, these two estimators just measure different things, the fixed effects estimator controls for every characteristic that remains constant, while your model with clustered standard errors does not. I don't see how you can compare the results of these two models. The point of presenting two models side by side is that (it implies that) you can compare models. If you can't compare those models, than presenting the models side by side will just result in confusion. The problem with a large proportion of dropped observations is that you may need to think again about to what population you are trying to generalize. For that reason I would look at wether those that drop out of your analysis analysis are in some sense different from those that are in the analysis in terms of your observed variables. If you are lucky there isn't much difference, and you can, with some arm waving, argue that it doesn't matter. If there are considerable differences, than I would just mention that, and at the very end of your paper discuss some hypotheses of how this may influence your estimates. Remember that you are trying to do something that is by definition impossible: get an empricial estimate of an effect while controlling for stuff that you haven't seen. So do not expect to get the right answer. What you should aim at is to look at your data as containing some information on the effect that you are interested in; it is not enough, but it is not zero either. There are now a variety of strategies you can follow to extract that information. Pick one, and do that one right. There are two reasons for that. First, using these strategies right is hard (not surprising as they try to solve an unsolvable problem...), so it really pays to focuss on one strategy. Second, it is much easier this way to write your paper in a way that it helps the reader to follow what data you have used and what information it contains that help you get an idea of what the effect of interest is (and what "information" comes from the (untestable) assumptions underlying your strategy). Others (or you in a different paper) can later use other strategies. After a sufficient body of literature has been assembled on this question, someone can try to summarize the different finding. 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/

**Follow-Ups**:**Re: st: Fixed effects logit model***From:*Abhimanyu Arora <abhimanyu.arora1987@gmail.com>

**AW: st: Fixed effects logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**References**:**st: Fixed effects logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

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