Re: st: What is the effect of centering on marginal effects?

[Nick Winter <njgwinter@gmail.com>]

Well, that depends how you define "trick."  I once reviewed a journal 
manuscript in which the authors simply doubled their data, "to show what 
the results might look like with more data."  No kidding.


Nick Winter

On 8/3/2012 10:10 AM, Swanquist, Quinn Thomas wrote:
> I agree with Nick and Will on this similar to my other post on a similar topic. Collinearity 'problem' is just a lack of power. There aren't any econometric tricks that create more information. The (often impossible) solution is gather more data.
>
> Quinn Swanquist
> qswanqui@utk.edu
>
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Winter
> Sent: Friday, August 03, 2012 9:48 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: What is the effect of centering on marginal effects?
>
> Indeed.
>
> I also wonder why people speak of "inflated" standard errors.  The standard errors are correct when the data are (highly) correlated: they are telling you that the data don't contain much information on the independent effects of the correlated variables.
>
>
> I've always enjoyed Goldberger's take on the "problem" of multicolinearity:
>
> "Econometrics texts devote many pages to the problem of multicollinearity in multiple regression, but they say little about the closely analogous problem of small sample size in estimation a univariate mean. Perhaps that imbalance is attributable to the lack of an exotic polysyllabic name for 'small sample size'. If so, we can remove that impediment by introducing the term micronumerosity."
>
> Goldberger, A. S. (1991). A Course in Econometrics. Harvard University Press, Cambridge MA.
>
> Quoted at more length here:
> http://davegiles.blogspot.com/2011/09/micronumerosity.html
>
>
> On 8/3/2012 8:13 AM, William Hauser wrote:
> > Dear all,
> >
> > I'm fairly confident that mean centering does nothing to resolve
> > collinearity.  I believe it does fool some of the diagnostic tools
> > though and that's probably why the belief that it somehow solves the
> > problem persists.  Mean centering simply shifts the collinearity onto
> > the intercept term.  Mean centering adds no new information to the
> > model and that's the problem - the data lack the necessary information
> > for the model to partial out the effects in a precise and stable
> > manner.  Perhaps this is effect is different for interaction terms,
> > but I fail to see how that's the case.
> >
> > Collinearity means the independent effects of the collinear variables
> > cannot be precisely estimated.  The point of interaction terms is that
> > they be analyzed jointly anyway.  The use of the margins and
> > marginsplot commands accomplish this with such ease (and polish) that
> > I would heartily recommend their use.
> >
> > Will
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