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From | Nick Cox <njcoxstata@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: fixed effects with multicollinearity |
Date | Fri, 29 Jul 2011 10:13:52 +0100 |
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 > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/