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Frank said he can't tsset his data, and that "SPSS offers users the
post regression estimation option to test the independence of
residuals terms using the Durbin-Watson test so that users can check
the independence assumption. "
Calculating a D-W statistic on, e.g., a cross-section is asinine, and
a program that lets you do it is a pretty lousy piece of software. As
the sort order of a cross section is arbitrary, I can change the
"serial correlation" between successive observations by just sorting
the data on some other key.
If you're saying that there are dates associated with your
observations, but they are not evenly spaced in calendar time, that's
a different issue (and one that sometimes causes people to say that
they can't tsset their data without creating unwanted gaps). Business-
daily data (e.g. stock market quotes) are not evenly spaced in
calendar time, but you can still put trading-day data on a time series
calendar.
As another poster recommended, Stata's cluster-robust covariance
matrix allows you to consider that the errors within each cluster are
arbitrarily correlated. You can calculate a cluster-robust VCE for
cross-sectional data (e.g. firms nested within industry clusters), but
that does not allow you to test for independence; it just gives you
standard errors that take account of such departures from i.i.d. But
without some structure you can't test for E e_i e_j = 0 for all i !=
j, because you have only one observation on each covariance in your
sample. Thus to test that the error covariance matrix is, for
instance, diagonal, you need to impose some additional structure on
the manner in which the off-diagonal elements are generated.
Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html
An Introduction to Stata Programming
| http://www.stata-press.com/books/isp.html
An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html
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