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Re: st: interpreting R-squared when constant has been supressed

From   Ulrich Kohler <[email protected]>
To   [email protected]
Subject   Re: st: interpreting R-squared when constant has been supressed
Date   Thu, 27 Sep 2007 14:41:25 +0200

Am Donnerstag, den 27.09.2007, 05:05 -0700 schrieb Lloyd Dumont:
> Hello.  I am running an OLS model in which
> observations fall into one of three mutually-exclusive
> and collectively-exhaustive categories.  For clarity
> in reporting, I thought it would be a good idea to
> suppress the constant and report slope estimates for
> all three dummies.
> If I run the model both ways (either with two dummies
> and the constant vs. with all three dummies and no
> constant), the estimates and the standard errors are
> what they should be, i.e., are the same in relative
> terms to one another in both models, same t-stats,
> etc.  But, without the constant, the R2 shoots up from
> something like .11 to something like .68.
> I sort of understand conceptually how this could
> happen--fit is now relative to zero than to the mean. 
> But...
> 1.  Is my understanding correct?
> 2.  How can I explain this succinctly?
> 3.  Am I being deceptive to report the .68?
> Thank you.  Lloyd Dumont

Use option "hascons" togehter with "nocons". 

On that occasion: There is an article in a German
econometrical/statistical journal, which heavily critizes  Statistical
packages (including Stata) for using the term "r-square" if the
regression is forced through the origin. The authors emphasize that
r-square cannot be interpreted in the usual way under this setting and
should be therefore renamed or omitted from the output. Only "Minitab"
does this. 

Ring/Ryll/Gaus (2006): Das Bestimmtheitsmaß R² bei linearen
Regressionsmodellen mit und ohne Intercept -- die Tücken der 
Statistikprogramme Wirtschaft und Statistik, 2006, 11, 607-612.

(The third author is Wilhem Gaus, not Carl Friedrich, btw)

Many regards


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