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Re: st: comparing coefficients across 2 models


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: comparing coefficients across 2 models
Date   Mon, 26 Nov 2012 13:21:26 +0100

On Mon, Nov 26, 2012 at 1:00 PM,  <francesco.mazzi@unifi.it> wrote:
> You also wrote "multicolinearity (high VIFs) is not a problem, it is just a
> description of an unfortunate state of the world, or at least, your data".
> Given that I got high VIFs for the interacted variables  and interaction
> term, do you think that my model would be biased if I do not deal with
> multicollinearity (e.g. using centering)?

Multicollinearity means that it is hard to distinguish between
different variables because they are highly correlated. As a
consequence there is little information present in your data. This
mainly leads to (unfortunate but correctly) large the standard error.
It usually won't lead to bias, but it may mean that the point
estimates become unstable (differ wildly from sample to sample).
Centering is still a good idea for interaction terms, as it makes the
effects more interpretable.

> Would a model with high VIFs in interaction terms sound statistically
> correct?

As I said before I will not give you a "statistical blessing". All I
can tell you is that you have to choose carefully and than face the
consequences of your choices.

-- Maarten

---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany

http://www.maartenbuis.nl
---------------------------------
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