Danny--
The answer is no, you cannot "trust this result" from a command that
does not allow cluster-robust estimation when you must use clustered
standard errors in the IV (ivreg2) estimation. If clustering is
important, and you have said that it is, and you have a weak
instruments problem, you must either improve the quality of your
instruments by adding/finding more excluded instruments, in which case
you probably want the LIML/CUE options on -ivreg2- (and overID tests),
or you can use a method of inference robust to the presence of weak
instruments that allows clustering, namely Anderson-Rubin tests/conf
regions. I discussed this in some detail at NASUG5, and some of the
material appears in the slides at
http://www.stata.com/meeting/5nasug/wiv.pdf and some in Stata Journal
7(4). On Anderson-Rubin tests/conf regions, see the Dufour and
Taamouti ref linked from http://www.stata.com/meeting/5nasug/wiv.pdf
(though I prefer constructing the confidence region rather than the
projection onto individual axes that they advocate--the latter can be
deceptive if, say, there are two variables measuring a similar
quantity and you can reject that both coefs are simultaneously zero
because the conf ellipse does not include the origin, but the
projections might both overlap zero). Tests are easier than
confidence regions for this approach, obviously.
On Thu, Feb 28, 2008 at 8:09 AM, Danny Cohen-Zada <[email protected]> wrote:
> I will try to edit my question to be clearer.
>
>
> Suppose that i estimate the following iv model with the cluster option.
>
> ivreg2 y1 x1 x2 (y2 = z1 ), ffirst cluster (x3)
>
>
> I tested that my instrument is weak (using the clustered f-statistic) and
> found that my instrument is weak.
>
> Then i run the conditional likelihood ratio test
>
> condivreg y1 x1 x2 (y2= z1)
>
> This command does not have the cluster option.
>
> Supose that i find that the p-value of this test is 0.000 . Can i trust this
> result even when i use clustered standard errors in my ivreg estimation.
>
> Thanks
>
> Danny
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