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st: HUGE Wald test statistics of testing multiple coefficients in a fixed effect model with cluster option


From   "J.J." <accjiang@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: HUGE Wald test statistics of testing multiple coefficients in a fixed effect model with cluster option
Date   Mon, 10 Mar 2008 15:27:18 -0400

Dear Statalisters,

My question is related to Daniel Simon's question in this unanswered
post: http://www.stata.com/statalist/archive/2006-03/msg00024.html. I
estimate a linear fixed effect model (about 3000 observations and 500
groups) with around 300 dummy variables. With the cluster option, the
F-statistics of testing the joint significance of these dummy
variables becomes HUGE (100,000+) even many of these coefficients are
dropped in the test. When I just use the robust instead of the cluster
option, the Wald test produces reasonable F-statistics. My main
purpose is to test the joint significance of these dummy variables in
the fixed effect model, should I drop the cluster option?

Given the suggestions by Johannes Schmieder, Mark Schaffer and Austin
Nichols in this post
(http://www.mata.dk/statalist/archive/2006-09/msg00782.html), I felt
it is absolutely necessary to use the cluster option with xtreg, fe.
However, Mark Schaffer and Austin Nichols
(http://repec.org/usug2007/crse.pdf) allude to the danger of testing
multiple coefficients after the cluster option. In their simulation,
the rejection rate increases to 1 as the number of coefficients
increases. I guess their results indicate that the Wald test in my
situation (cluster option and so many variables in the model) is not
valid. What should I do? Any suggestions will be highly appreciated.

One solution is to test if I need the cluster option using the not yet
available xtcltest (Mark and Austion, when is this program
available?). If I do need the cluster option, the next option is to
get rid of some of the dummy variables.

J. J.
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