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st: Re: orthogonal regression


From   Kit Baum <[email protected]>
To   [email protected]
Subject   st: Re: orthogonal regression
Date   Thu, 14 Dec 2006 09:53:32 -0500

Instrumental variables procedures are often used to deal with problems of errors-in-variables, and a common instrument in that case is the rank of the regressor. Of course it is not 'independent' of the regressor itself--if it was it would make a lousy instrument.

If you have a model in which y1 and y2 are jointly determined, you need some variables not in the equation to estimate the relationship in which they both appear. That could be instrumental variables or a k-class estimator such as LIML. IV is just a special case of the k- class where k==1. But I do not think that consistent estimation of the "orthogonal regression" (k ~= 1) model can be performed (by, e.g., Nick Cox's -sdline-) if the regressor is correlated with the error.


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 Dec 14, 2006, at 2:33 AM, statalist-digest wrote:


One of the problems I see in this solution is that in the first model
iv1 is not independent of the errors (it is just the ranking for y1)
and similarly for iv2 in the second model.
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