Hello, everyone. As many of you know, I have been
estimating (using �xtreg- and the cluster option) a
random effects model on an unbalanced panel in which
there are about 15 clusters, most observed about 15
times. That is, this is not the typical situation in
which in which the number of clusters is significantly
larger than the number of time periods.
Two concerns:
First, I am concerned that FE is more appropriate than
RE. That is a problem, because just about all the
regressors I care about are time constant. (I think
this may mean �tough luck� for me, I realize.)
Second, I am concerned with the potential for small
sample bias in my variance estimates. I thought a
good way to deal with this would be to bootstrap my
standard errors (using �xtreg- , the cluster option,
and the vce(bootstrap) options). Now, I am a novice
with the bootstrap, but my initial attempts yielded
confidence intervals, not surprisingly, far less
favorable than those yielded under the normality
assumption. (This did not improve much as I increased
the number of reps.)
So, does that just further demonstrate that my RE
estimates are inconsistent and that there is little I
can do about it?
Or, is there a way to use the bootstrap to better
determine the actual/observed error structure, and
then re-estimate the model by imposing this �revealed�
structure upon it?
Thank you for your thoughts/suggestions. Lloyd Dumont
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