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Re: st: Why no sum of squared residuals in reg output after robustor cluster?

From   SamL <[email protected]>
To   [email protected]
Subject   Re: st: Why no sum of squared residuals in reg output after robustor cluster?
Date   Fri, 5 Mar 2004 08:08:53 -0800 (PST)

Am I the only person saddened by this blatant rip-off of statacorp?  And
from a person employed at a religious institution, no less?  Anyone have
any ideas how stata can prevent this from happening and thus preserve the
wonderful model of a company they are?

Sign me,

Despite appearances, there is *no* sarcasm in my voice,

On Fri, 5 Mar 2004, Richard Williams wrote:

> At 08:41 PM 3/3/2004 -0600, [email protected] wrote:
> >This is probably easy, but I have been wondering...
> >When you add the comand robust or cluster after the regression you don't
> >get in
> >the regression output the information about the explained sum of squares, the
> >residual sum of squares and the total sum of squares, information that you
> >usually get if you perform regress without the robust or the cluster command.
> >Why is it so?
> >Is it that the residual sum of square is not reliable when you correct for
> >heteroscedasticity with the Huber/White/Sandwich estimator, or if you cluster
> >the standard errors?
> >But then, how is the R-square (whch is reported, but not the adjusted one)
> >calculated?
> This is a good chance to try out my scanner and optical character
> recognition software.  Here is what the manual says:
>  From p. 337, Stata 8 Reference Manual N-R (from the documentation for the
> regress command):
> Technical Note
> When you specify robust, regress purposefully suppresses displaying the
> ANOVA table, as it is no longer appropriate in a statistical sense even
> though, mechanically, the numbers would be unchanged. That is, sums of
> squares remain unchanged, but the meaning you might be tempted to give
> those sums is no longer relevant. The F statistic, for instance, is no
> longer based on sums of squares; it becomes a Wald test based on the
> robustly estimated variance matrix. Nevertheless, regress continues to
> report the R^2 and the root MSE even though both numbers are based on sums
> of squares and are, strictly speaking, irrelevant. In this, the root MSE is
> more in violation of the spirit of the robust estimator than is R^2. As a
> goodness-of-fit statistic, R^2 is still fine; just do not use it in
> formulas to obtain F statistics because those formulas no longer apply. The
> Root MSE is valid as long as you take its name literally - it is the square
> root of the mean square error. Root MSE is no longer a prediction of sigma
> because there is no single sigma; the variance of the residual varies
> observation by observation.
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