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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