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From |
Abhimanyu Arora <abhimanyu.arora1987@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Fixed effects logit model |

Date |
Mon, 19 Jul 2010 17:56:33 +0200 |

Thanks Maarten, for a very useful and practical response. On Mon, Jul 19, 2010 at 3:50 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- 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/ > * * 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: Fixed effects logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**Re: st: Fixed effects logit model***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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