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AW: st: Multicollinearity: VIF vs. variance decomposition


From   "Lucas Bremer" <bremer@bwl.uni-kiel.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   AW: st: Multicollinearity: VIF vs. variance decomposition
Date   Wed, 18 Nov 2009 11:54:25 +0100

Some are significant.

My sample has 250 individuals with 6 observations over time.

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von JOHN ANTONAKIS
Gesendet: Mittwoch, 18. November 2009 11:48
An: statalist@hsphsun2.harvard.edu
Betreff: Re: st: Multicollinearity: VIF vs. variance decomposition

Hi Lucas:

Are the coefficients of interest significant? If so, despite a high 
multicollinearity, if the coefficient of interest are significant you 
probably have a large enough sample size to indentify their unique 
effects (what is your n-size and cluster size?).

Best,
J.

____________________________________________________

Prof. John Antonakis
Associate Dean Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 18.11.2009 08:51, Lucas Bremer wrote:
 > Dear all,
 >
 > I have some problems in detecting harmful multicollinearity in my random
 > effects panel regression.
 >
 > To use the VIF as an indicator I made a linear regression and had a 
look at
 > the VIFs (estat vif)
 >
 > Here are 6 (out of 20) variables with VIFs ranging from 8 to 15. Now I'm
 > interested between which of my independent variables problems due to
 > multicollinearity arise.
 >
 > Therefore I used the variance decomposition (coldiag2). Now I'm a 
little bit
 > confused about the results because two of my variables have a very high
 > VIF>10, but in the variance decomposition there is no other variable 
with a
 > variance proportion > 50 for the same eigenvalue. This indicates that 
there
 > is no quasi-linear relationship for this variable.
 >
 > Which measure can I trust if they have from my point of view 
contradicting
 > results?
 >
 > By the way, someone told me that it is quite normal to have higher 
VIFs in a
 > panel regression and that the thresholds could be higher? Is this right?
 > What intuition is behind that statement?
 >
 > Thanks in advance for your help,
 > Lucas
 >
 >
 >
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