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st: Re: ivreg2


From   Kit Baum <baum@bc.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: ivreg2
Date   Sun, 4 Feb 2007 09:00:15 -0500

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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