My recollection is that this was posted by David Drukker (apologies, David, if I'm in correct in
attributing this to you), and the advice was indeed not to drop singleton groups.
It's not clear to me why you would ever want to drop singleton groups. A group with one member is
an observation that still has some information in it. If you throw away that observation, you're
throwing away information unnecessarily.
Another way to put it is to consider what happens when you estimate the fixed effects model after
transforming the data into mean-deviation form, so that all the FEs disappear. The FE estimation
is obtained by simple OLS on the transformed data. After the MD transformation, you can't tell
just by looking at the data which datapoints are several observations from one group, and which
datapoints are observations from groups with just one member; in a sense, after the transformation,
they're all independent observations. You wouldn't want to throw away observations here, since the
attribute that distinguishes these obs (they are the sole representatives of their groups) has been
transformed out of the data.
--Mark
Quoting Guillaume Frechette <[email protected]>:
> Dear Statalisters: I thought I read on the list not so long ago someone who
> said that when estimating FE one shouldn't drop groups that do not have
> variation (I looked for it in the archive but couldn't find it). Did I
> invent this? At any rate, could someone tell me what is the 'best'
> procedure, keeping all groups or only the ones that have variation, and what
> is the logic for the answer. I would also appreciate it if someone could
> point me to a book or article that spells this out in writing.
>
> Thanks in advance,
>
> g
>
>
>
>
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Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: [email protected]
web: http://www.som.hw.ac.uk/ecomes
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