Hi Lucas:
Seems like you have a relatively large sample; however, those "some"
that are significant, are they the substantively important ones? Are
some of the substantively important not significant? If it is the latter
then multicollinearity might be a problem (in which case I would create
indexes of the variables are correlate strongly, if it is substantively
and theoretically defensible to do so).
HTH,
John.
____________________________________________________
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
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____________________________________________________
>
>
> On 18.11.2009 11:54, Lucas Bremer wrote:
>> 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
>> 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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