re:Re: Re: st: Direction of the effect of the cluster command on the

[Christopher Baum <kit.baum@bc.edu>]

<>
Austin wrote

Useful to think of super-obs but not quite right.  If you have 50
clusters and 100 regressors (with a few thousand obs) but you are only
interested in testing one coefficient, you will typically be fine,
i.e. you will have negligible bias in the SE thus getting correct
inference on average with the CRSE, and it may often be the case that
no alternative approach gets you correct inference (except resampling
clusters for a cluster-robust bootstrap).  So estimating a regression
with 50 obs and 100 coefficients is not quite the right analogy--more
useful to think of the "effective" sample size as between M (number of
clusters) and N (number of obs), computable using "roh" per Kish, L.
(1965), Survey Sampling, New York: Wiley (note that the CRSE is also
the standard svy estimator).

Quite so, Austin; unless you are interested in all the coefficients in a regression, you may not be that concerned about the number of 'super-observations'. The effective sample size is indeed a more useful construct.

However it should be noted, for those not that familiar with cluster-robust VCEs, that Stata uses the number of 'super-observations' minus 1 when it reports test statistics. For instance,

webuse grunfeld
reg invest mvalue kstock time, clu(company)

reports an ANOVA F-stat based on 3 and 9 df, where 9 is 10 companies - 1.  Likewise, the t-stat pvals are those for 9 df. This is rather important for the original poster of this thread, who was working with 4 clusters (and 3 denom. d.f. in the F, and 3 d.f. in the t).

Cheers
Kit



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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