Some are significant.
My sample has 250 individuals with 6 observations over time.
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von JOHN ANTONAKIS
Gesendet: Mittwoch, 18. November 2009 11:48
An: [email protected]
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
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____________________________________________________
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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