Re: st: Multicollinearity: VIF vs. variance decomposition

 From JOHN ANTONAKIS To statalist@hsphsun2.harvard.edu Subject Re: st: Multicollinearity: VIF vs. variance decomposition Date Wed, 18 Nov 2009 11:48:04 +0100

```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.

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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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```