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Re: st: comparing coefficients across 2 models

 From francesco.mazzi@unifi.it To statalist@hsphsun2.harvard.edu 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?

Francesco

----- Message from maartenlbuis@gmail.com ---------
Date: Mon, 26 Nov 2012 11:34:52 +0100
From: Maarten Buis <maartenlbuis@gmail.com>
Subject: Re: st: comparing coefficients across 2 models
To: statalist@hsphsun2.harvard.edu

```
```On Mon, Nov 26, 2012 at 11:08 AM,  <francesco.mazzi@unifi.it> 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
(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

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

http://www.maartenbuis.nl
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```

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