Hi Kit,
Thanks for all your help.
Sandra
***********************************************
Sandra Mortal
Assistant Professor of Finance
516 Cornell Hall
University of Missouri
Columbia, MO 65211
Office: (573) 884-1684
Fax: (573) 884-6296
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-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Kit Baum
Sent: Sunday, February 04, 2007 8:00 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Re: ivreg2
1) That sounds sensible.
2) As I understand it the distribution of the S-Y critical values differ
depending on, among other things, the underlying estimator. So I would
not make much of the fact that the CVs differ between IV-GMM and LIML.
3) In the context of a single FSR, the notion that the ANOVA F should
exceed 10 is a rule of thumb suggesting that the FSR is reasonably
strong. The first example in ivreg2 help shows a model with a single
endogenous regressor for which F = 13.79 (zero pvalue). The Anderson
canon. corr. LR stat. is 54, with a pvalue of 0, ad the Cragg-Donald min
eigenvalue stat is 56, with a pvalue of zero. The null for each of these
tests is underidentification, with rejection indicating that the model
is identified. The latest version of ivreg2 (soon to be
released) reformats these results to make the underlying hypotheses
clear. However that same model has a significant Sargan test statistic,
suggesting that the overidentifying restrictions are soundly rejected by
the data. This implies that the instruments are inadequate and must be
replaced. So you can have sufficiently strong instruments but still fail
the Sargan (or Hansen J, in IV-GMM) test.
I would worry about that in your model.
Kit
Kit Baum, Boston College Economics
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
On Feb 4, 2007, at 2:33 AM, Sandra wrote:
> (1) If the bias of the IV estimates cause them to approach the
> (inconsistent) OLS estimates (which are significantly positive), then
> if I get larger IV estimates (compared to the OLS estimates) does that
> mean my estimates would be even larger if I had strong instruments?
> In other words, would that imply my IV estimates are biased downward?
>
> (2) I read a review by Stock and co-authors, and they mention that if
> I estimate my equation using liml, then my results will be more robust
> to weak instruments, further, I noticed that the Stock-Yogo critical
> values are much lower, when I use LIML. Am I making a correct
> intepretation of their text?
>
> (3) In the review by Stock and co-authors, they mention that the rule
> of thumb is that an F-test should be greater than 10. They also
> mention in the context of multiple instrumental variables the
> Cragg-Donald F statistic and the Stock-Yogo critical values. Does
> this Cragg-Donald statistic also work in a one instrument context?
> This is because, when I look at this statistic my instruments seem to
> be higher than the 10% Stock-Yogo critical value. If each of these
> statistics work as a sufficient condition (rather than a necessary
> condition) for having strong instruments, then I would rather report
> the Cragg-Donald statistic, considering this is statistically sound.
>
> My problem is that it is very tough to get exogenous instruments for
> my variable, and this is the only instrument I have managed to come up
> with, so I would like to stick to it, if at all possible.
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