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st: RE: collinearity adjustment


From   Rodolphe Desbordes <rodolphe.desbordes@strath.ac.uk>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: collinearity adjustment
Date   Mon, 15 Mar 2010 10:11:15 +0000

Fabio,

We recently had a thread about multicollinearity. If by "problem of multicollinearity", you mean "the problem when an approximate linear relationship among the explanatory variables leads to unreliable regression estimates" (Verbeek (2008), "A Guide to Modern Econometrics", p.43), then centering will not solve this problem.

Good references on this issue:

http://homepages.nyu.edu/~mrg217/pa_final.pdf

http://www.bus.ucf.edu/echambadi/Review/msmean-center.pdf

http://www.press.umich.edu/titleDetailDesc.do?id=206871



Rodolphe

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Fabio Zona
Sent: lundi 15 mars 2010 08:28
To: statalist@hsphsun2.harvard.edu
Subject: st: collinearity adjustment

Dear Statlists

centering variables around their means is a standard procedure to solve the problem of multicollinearity which arises when dealing with interaction terms and linear/quadratic terms.

I tried to center my variables, but, after centering, -collin- commands tell VIF values are still very high; maybe it depends on the fact that in my database there are thousands of zero-observations, and the mean is very close to zero: as a consequence, subtracting the mean from the variable does not change it enough to solve the problem of multicollinearity.

Is my interpretation correct? Any idea on how I might solve this problem?

Thanks a lot


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