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RE: st: Testing of differences in R-square

From   "Lachenbruch, Peter" <>
To   "''" <>
Subject   RE: st: Testing of differences in R-square
Date   Wed, 31 Mar 2010 08:09:13 -0700

To amplify a bit - this is specifically demonstrated in Draper and Smith as "extra SS" - and most likely in others.  My initial reaction was the same as Steve's, but by the time I read it, the answer was already on the list.  The use of e(sample) was an idea that hadn't occurred to me (of course, all data in regression are complete!  (-:) )


Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001

-----Original Message-----
From: [] On Behalf Of Steve Samuels
Sent: Tuesday, March 30, 2010 3:24 PM
Subject: Re: st: Testing of differences in R-square

"R-squares is equivalent to assessing a difference between the mean
square errors."

I should clarify: This statement is true because the R-squares are
being computed on the same data and for the same response variable. To
ensure this condition, restrict the sample to observations with
non-missing values for all predictors in the two regressions.

On Tue, Mar 30, 2010 at 10:24 AM, Steve Samuels <> wrote:
> Vuong's test is likelihood based and corrects for the differing number
> of parameters in two models. For your problem you can directly
> compared adjusted R-squares, which also correct for the number of
> parameters.. However assessing a difference between two adjusted
> R-squares is equivalent to assessing a difference between the mean
> square errors. So I suggest you bootstrap a difference in log mean
> square errors. Because you want to bootstrap two regressions, you'll
> need to write your own program. See:
> Steve

Steven Samuels
18 Cantine's Island
Saugerties NY 12477
Voice: 845-246-0774
Fax: 206-202-4783
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