st: Re: goodness of instruments

[Kit Baum <baum@bc.edu>]

Dear Alessia

Your comment #1 is a FAQ. Instrumental variables does not work by  
specifying which instruments apply to which X variables. You have a  
set of X and a superset Z, including the exogenous elements of X.  
Please see Ch.8 in my book, or Baum-Schaffer-Stillman Stata Journal  
2003, available in working paper form from the URL below. Some  
collinearity among the Zs does not invalidate their use; you're just  
projecting Xs on Zs, and the goodness of fit of that projection is  
not affected by intercorrelations among the Zs as long as they are  
not perfect.

You should use xtivreg2 (findit xtivreg2) in order to have access to  
all of the diagnostic techniques available in ivreg2. Using xtivreg2,  
you can use the endog() option to evaluate whether specific  
regressors must be treated as endogenous, and employ the test of  
overidentifying restrictions (Sargan-Hansen) to consider the quality  
of the instruments, as well as tests for weak instruments.


Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html


On May 24, 2007, at 6:54 AM, alessia matano wrote:


> Hi,
>
> I would like to ask you one thing on the goodness of instruments.
> I have six variables that I consider endogeneous. I m doing the first
> stage with xtivreg. and I would like to ask you:
>
> 1. Is that correct to instrument each one with its own instrument and
> with the exogeneous ones (correcting then the standard errors)? i.e .
> not using in the first stage the instruments related to the other
> variables (I am afraid on problems of possible multicollinearity).
>
> 2. If I run the first stage regression with only the exogeneous and
> then with the exogeneous and the instruments, I can I see if the
> instrument is good in that case??
>
> how should I compare the r squared?
>
> Thanks a lot
> alessia
> p.s. my regression is done in fixed effects, with aweights, because
> some of the variables come from a microlevel collapse (my dependent
> var overall, but not my independent of interest)
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