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Re: st: Correcting Multicollinearity


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Correcting Multicollinearity
Date   Thu, 29 Mar 2012 22:08:01 -0500

Ridge regression, a family of methods for remediating multicollinearity, is implemented by -ridgereg- (SSC). A serious user should, at a minimum, study the following reference, given in the -help-.

 Evagelia, Mitsaki (2011) "Ridge Regression Analysis of Collinear Data", 
  http://www.stat-athens.aueb.gr/~jpan/diatrives/Mitsaki/chapter2.pdf

Steve
sjsamuels@gmail.com



On Mar 29, 2012, at 8:18 AM, Nick Cox wrote:

Another answer is that multicollinearity has computational,
statistical and scientific sides.

It's (mostly) Stata's job to do the best with the computation it can
given (tendencies to) multicollinearity among the predictors.

It is your job to think about choice of predictors.

But you can think about the statistical side by looking at the
structure of interdependence among predictors statistically. One part
of the statistical world is seemingly obsessed by the idea that this
must mean some yes-or-no test yielding a P-value or some omnibus,
factotum or portmanteau statistic quantifying how bad the problem is;
their punishment is to miss what may be learned from looking at
correlations and e.g. -graph matrix-.

Scientifically, what you know about the problem should guide revised
idea about whether the predictor choice is overkill.

Nick

On Thu, Mar 29, 2012 at 12:33 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Thu, Mar 29, 2012 at 1:22 PM, D. Demetriou wrote:
>>> Is there any Stata tool to (partly) account for this
>>> defect(multicollinearity) after this has been indicated by, for example, vif
>>> or collin commands?
> 
> No, you can only do something about that before collecting your data
> by choosing a specific design. After you have collected the data, the
> correlations in the data are just what they are and you will just have
> to learn to live with them.
> 
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