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Rép. : Re: st: Multicollinearity and logit


From   "Herve STOLOWY" <stolowy@hec.fr>
To   <statalist@hsphsun2.harvard.edu>
Subject   Rép. : Re: st: Multicollinearity and logit
Date   Wed, 19 Mar 2008 11:21:41 +0100

Dear Richard:

I thank you for your detailed reply.

For your information, I discovered the -vif, uncentered- because I had typed -vif- after -logit- and got the following error message:

not appropriate after regress, nocons;
use option uncentered to get uncentered VIFs

Best regards

Herve



***********************************************************
Professeur/Professor
President of the French Accounting Association (AFC)
HEC Paris
Departement Comptabilite Controle de gestion / Dept of Accounting and Management Control
1, rue de la Liberation
78351 - Jouy-en-Josas
France
Tel: +33 1 39 67 94 42 - Fax: +33 1 39 67 70 86
mail: stolowy at hec dot fr
web: http://www.hec.fr/stolowy
>>> Richard Williams <Richard.A.Williams.5@ND.edu> 19/03/08 0:30 >>>
At 07:37 AM 3/18/2008, Herve STOLOWY wrote:
>Dear Statalisters:
>
>I have a question concerning multicollinearity in a logit regression.
>
>How could I check multicollinearity? I tried several things.
>
>- Correlation matrix: several independent variables are correlated.
>
>- Logit regression followed by -vif, uncentered-.  I get high VIFs
>(maximum = 10), making me think about a high correlation.
>
>- OLS regression of the same model (not my primary model, but just to
>see what happens) followed by -vif-: I get very low VIFs (maximum = 2).
>
>- -collin- (type findit collin) with the independent variables: I get
>very low VIFs (maximum = 2).
>
>What is better? I am puzzled with the -vif, uncentered- after the logit
>which returns very high VIFs.

I'm surprised that -vif- works after logit; it is not a documented 
post-estimation command for logit.  Given that it does work, I am 
surprised that it only works with the -uncentered- option.  I wonder 
if this is a bug and if the results mean anything.

I always tell people that you check multicollinearity in logistic 
regression pretty much the same way you check it in OLS 
regression.  Multic is a problem with the X variables, not Y, and 
does not depend on the link function.  So, the steps you describe 
above are fine, except I am dubious of -vif, uncentered-.


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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