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st: Re: panel data: xtrege, re vs. xtreg, fe vs. regress...cluster
-reg, cluster(id)- produces OLS as the same as -reg- (try it to check it)...
the only difference is that the standard errors are computed with the
sandwich estimator in the first case. -xtreg, fe- or -xtreg, re- consider
the existence of unobservable in the model. RE works under the assumption
that the unobservable is uncorrelated with the other variables in the model,
FE does not need that.
If the effect that you got from OLS is not in FE model, this means that
characteristics specific to the firms are more important than the ones that
you have in your regression. A simplified answer is to say that "the name"
of the firm is more relevant that ROA, reputation, etc. Sorry if this is bad
news for you.
----- Original Message -----
From: "Michael Pfarrer" <Michael_Pfarrer@rhsmith.umd.edu>
Sent: Sunday, March 11, 2007 1:36 PM
Subject: st: panel data: xtrege, re vs. xtreg, fe vs. regress...cluster
I know this topic has been debated for years on the listserv with many
helpful responses, but I thought I'd ask again to help me clarify things.
I have a dataset of 291 firms over 15 years (N=4365). What, in essence, is
the "best" model to run for this analysis? I understand the difference
between fe and re and the use of the Hausman test, which, for me, is n.s.
But is there really an inherent difference between xtreg, re and regress,
cluster(id)? The OLS regress, cluster(id) normally gives me "better"
results, but I certainly want to understand if it is "correct". I also
understand that xtreg, re can decompose into OLS. So, if you had these data
across this panel with a continuous DV and continous and binary IVs, what
would you run? The DV is the Cumulative Abnormal Return from a 3-day event
window (-1, +1)and the IVs are firm financial characteristics--ROA, volume,
reputation dummy, etc.
Michael D. Pfarrer
Department of Management and Organization
Robert H. Smith School of Business
University of Maryland
College Park, MD 20742
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