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st: Question about finite-sample adjustment for cluster-robust estimation
Hi everybody. David Roodman and Kit Baum spotted a small bug in
ivreg2 when the cluster-robust option is used - the F-stat differs
from that reported by official Stata because the degrees of freedom
adjustment is different. (We'll post a bug fix shortly.)
But this led to a question of why official Stata makes the finite-
sample adjustment it does for cluster-robust estimation, and maybe
there are some survey specialists out there who can explain it.
The manual (under Regress) states that if the option chosen is just
-robust-, the finite sample adjustment for the var-cov matrix is
where N is the number of observation and k is the number of
regressors including the constant.
If the option chosen is -cluster-, then the adjustment is
(N-1)/(N-k) * M/(M-1)
where M is the number of clusters.
The intuition is clear enough - when the number of clusters is small,
the standard errors can get big - but why M-1? Why not, for example,
M-k? (The logic for this alternative is that the rank of the var-cov
matrix is M-k when the cluster-robust option is chosen, and if M-k is
small then the standard errors should be big.) Does anybody know of
any Monte Carlo evidence on this? Inquiring minds want to know....
Prof. Mark E. Schaffer
Centre for Economic Reform and Transformation
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS UK
44-131-451-3485 CERT administrator
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