st: Re: orthogonal regression

[Kit Baum <baum@bc.edu>]

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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