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


From   Alessandro Freire <alessandro.freire@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: What is the effect of centering on marginal effects?
Date   Fri, 3 Aug 2012 12:02:10 -0300

I think Ulrich mentioned the most important issue regarding
interactive models. The coefficients and standard errors shown in
traditional tables are simply not enough for a researcher to make
relevant interpretations of conditional relationships between
variables. Plotting graphics can provide the full range of the
marginal effect of X conditioned by Z as well as the confidence
interval in which the interaction is statistically significant.

Aiken & West's  "Multiple Regression: Testing and Interpreting
Interactions" (1991) is a great read on the graphical demonstration of
marginal effects. Goldberger's quote mentioned by Nick is on the
textbook of both Wooldridge's "Introductory Econometrics" and
Gujarati's "Basic Econometrics".

On Fri, Aug 3, 2012 at 11:15 AM, Nick Winter <njgwinter@gmail.com> wrote:
> 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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>>
>>
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