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

[Richard Williams <Richard.A.Williams.5@nd.edu>]

At 08:41 PM 3/3/2004 -0600, dbdewalq@uchicago.edu 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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