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Re: st: Fixed effects logit model
Abhimanyu Arora <email@example.com>
Re: st: Fixed effects logit model
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 <firstname.lastname@example.org> 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 L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
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