I am iteratively estimating a model and then using the bootstrap procedure
to derive the standard errors and t-stats. The procedure works fine on a
moderately sized sample, but each replication of the bootstrap takes over
three hours on my full sample (over 1 million obs.). In order to speed up
the process I would like to perform each repetition of the bootstrap on a
subsample of the data, say 100,000 observations. This can of course be
done by setting the size() option in bootstrap. The FAQs warn against
this, however, saying "the standard error estimates are dependent upon the
number of observations in each replication. Here, on average, we would
expect the variance estimate of b[foreign] to be twice as large for a
sample of 37 observations than that for 74 observations. This is due
mainly to the form of the variance of the sample mean."
Is there some straightforward correction I can make to get the correct
standard errors when using the size() option in bootstrap?
Alternatively, I thought about just taking the estimated coefficients from
each repetition of the bootstrap and then forming an empirical distribution
from these estimates to get the standard errors. But I am not quite sure
how to accomplish this in Stata.
Any help on these ideas or alternative solutions would be greatly appreciated.
Tim
Tim R. Sass
Professor Voice: (850)644-7087
Department of Economics Fax: (850)644-4535
Florida State University E-mail: tsass@coss.fsu.edu
Tallahassee, FL 32306-2180 Internet: http://garnet.acns.fsu.edu/~tsass
