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Re: Fw: st: comparing coefficients across models

 From David Hoaglin To statalist@hsphsun2.harvard.edu Subject Re: Fw: st: comparing coefficients across models Date Tue, 7 Aug 2012 18:14:05 -0400

```Dalhia,

The VIF that I am familiar with (from literature on regression
diagnostics) applies separately to each variable in the regression, so
I'm not sure which variable's VIF you are reporting.  (The basic idea
is that you can regress each predictor on the set of other predictors
and get an R^2.  You can interpret the corresponding 1 - R^2 as the
"usable fraction" of that predictor.  The VIF for a predictor is the
reciprocal of its 1 - R^2.)

A VIF greater than 10 is not encouraging, but I don't think of 10 as
the threshold for high collinearity, with larger values considered
unacceptable.  In your model, the VIF of interest is the one for
x3*group_dummy.  If you have the actual VIF for that predictor, I
would like to see it, along with the VIFs for the other predictors.

A "large" VIF is only part of the story.  Further analysis, based on
the singular value decomposition, can show which predictors are
involved in the near dependency.

David Hoaglin

On Tue, Aug 7, 2012 at 10:31 AM, Dalhia <ggs_da@yahoo.com> wrote:
> David, thanks once again for helping me think this through. I reran with regress, and the VIF coefficient is less than 10 when I run model separtely for the two groups, and higher than 10 when I rerun with whole sample, and with the interaction with the dummy. I also checked the multicollinearity for the fixed effects panel model by running vif, uncentered (same finding as above).

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