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Re: st: comparing coefficients across 2 models

Subject   Re: st: comparing coefficients across 2 models
Date   Mon, 26 Nov 2012 13:00:14 +0100

Hi Maarten,

thanks for your help and suggestions. I really appreciate the fact that you kind of "ranked" the solutions for dealing with multicollinearity.

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

Thanks for your help,

----- Message from ---------
    Date: Mon, 26 Nov 2012 11:34:52 +0100
    From: Maarten Buis <>
 Subject: Re: st: comparing coefficients across 2 models

On Mon, Nov 26, 2012 at 11:08 AM,  <> wrote:
I am struggling with a model and I am interested in understanding if an
independent variable (x1) changed its impact on the dependent variable (y)
following an event (prepost=0 if pre event; prepost=1 if post event).

I know that I could run a model considering the interaction of the two.
xi: regress y c.x1##i.prepost x2 x3, robust cluster(firmcluster)
The problem is that I got really high VIFs (above 30!!)

I investigate the possibility of centering or orthogonalising the variables
(I saved the residuals of regress x1_prepost_interaction x1 prepost). I got
quite nice results.
Do you think this is ok and acceptable? Can you suggest other methods for
overcoming the problem of multicollinearity?
Moreover, I thought to split the model and run the following two commands

Modeling is always a judgement call, so we cannot give you our
"statistical blessing" and say your model is correct. That is in the
end your decision and your decision alone. Moreover, multicolinearity
(high VIFs) is not a problem, it is just a description of an
unfortunate state of the world, or at least, your data.

Having said that, centering is usually an excellent idea; it often
helps interpretability of results and reduces numerical problems, so
that is a win-win situation. Orthogonalising variables can reduce
numerical problems, but often makes it harder to interpret the
results, so that is a trade-off you need to make. Estimating separate
models as a "solution" to solve multicolinearity is a horrible idea;
it does not make the multicolinearity go away, it just makes it harder
to detect.

Hope this helps,

Maarten L. Buis
Reichpietschufer 50
10785 Berlin
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