Yes, if by drop you mean omit from the model (not -drop- from the
dataset). Best to do it on scientific, substantive or practical
grounds if there is a choice.
On Fri, Jul 29, 2011 at 10:07 AM, Reddy, Colin <creddy@uj.ac.za> wrote:
> Thanks Daniel
> So I guess the best is to drop one of the collinear variables.?
>
> Colin ________________________________________
>
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of daniel klein [klein.daniel.81@googlemail.com]
> Sent: 29 July 2011 11:01 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: fixed effects with multicollinearity
>
> Colin,
>
> please note that mean centring does nothing to solve the underlying
> problem of collinarity (if there is something like that)., see e.g.
> Echambadi and Hess (2007) or Shieh, G. (2011).
>
> However, in another post
> (http://www.stata.com/statalist/archive/2011-04/msg01204.html) Maarten
> Buis pointed out that in the special case, where a variable is
> interacted with itself, to model non-linearities, centering can help.
>
>
> Echambadi and Hess (2007). Mean-Centering Does Not Alleviate
> Collinearity Problems in Moderated Multiple Regression Models.
> Marketing Science, 26: 438-445
>
> Shieh, G. (2011). Clarifying the role of mean centring in
> multicollinearity of interaction effects. British Journal of
> Mathematical and Statistical Psychology, 64: 1-12
>
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