Just a quick word of thanks to Vince and also to Kit for such a thorough
explanation and follow-up.
For what it's worth, robust SEs can also be generated using an artificial
regression approach (see Wooldridge's u/g textbook). When I did this, the
SEs agree with Stata's (and hence not with RATS's). Arguably, Stata's
robust SEs in this case are the more standard.
--Mark
Quoting Christopher F Baum <[email protected]>:
> On Tuesday, July 1, 2003, at 02:33 AM, Vince wrote:
> >
> >
> > Mark Schaffer <[email protected]> is estimating a model with
> an
> > indicator
> > (dummy) variable that is 1 in only a single observation and 0
> > everywhere else
> > and he wants an explanation for some things he notices about the
> > variance-covariance matrix,
>
> Thanks to Vince Wiggins for his cogent explanation of what is going
> on
> in this case (where the VCV becomes less than full rank). He and I
>
> corresponded offlist about the results I had posted
> to Statalist using RATS, in which I found it strange that the robust
>
> standard errors did not agree, even in the case where the dummy
> (sorry,
> indicator) is excluded. We agreed that the difference in the latter
>
> case (e.g. regress weight length, robust) was a degrees-of-freedom
>
> issue. If one scales RATS' robust covariance matrix to divide by
> (N-k)
> rather than N, the two programs agree exactly.
>
> With the dummy included, they do not; but inverting RATS' covariance
>
> matrix leads to a (slightly different) matrix of rank 2. Since RATS
>
> indicates that a generalized inverse is being used, and there is
> more
> than one way to generate a g-inverse, close in this case may be good
>
> enough (as it is in the case of horseshoes and hand grenades).
>
> Kit
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: [email protected]
web: http://www.sml.hw.ac.uk/ecomes
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