Could I have more information on the "sigmamore" option. I couldn't
find it using the search process.
Thanks.
>>> [email protected] 11/06/02 12:06PM >>>
Dawn,
If you're using -hausman- after ivreg, you won't get the "failure to
meet the asymptotic assumptions" problem if you use the -sigmamore-
option. This guarantees that the matrix difference in the middle of
the standard Hausman test statistic is positive definite.
Even simpler, just to install -ivendog- from ssc-ideas. This will
give you both the Wu-Hausman artificial regression F stat, and the
(Durbin-Wu-)Hausman chi-sq statistic, after estimation using -ivreg-.
Hope this helps.
--Mark
Date sent: Wed, 06 Nov 2002 11:53:37 -0500
From: "WILLIAM EVEN" <[email protected]>
To: <[email protected]>
Subject: st: Re: hausman test
Send reply to: [email protected]
> An alternative approach to the Hausman test is to include the
residual
> from the reduced form equation into the primary equation and perform
a
> t-test on its significance. This avoids any problems with the
> differences in the variance covariance matrices being non-positive
> definite that one can encounter in the original formulation of the
> test.
>
>
>
> Bill Even, Professor
> Department of Economics
> Miami University
> Oxford, OH 45056
>
> Phone: 513-529-2865
> Fax: 513-529-6992
> home page: www.sba.muohio.edu/evenwe
> e-mail: [email protected]
>
> >>> [email protected] 11/06/02 10:29AM >>>
> Hi,
>
> I'd like to know if anyone has any suggestions on how to proceed
when
> you cannot perform the Hausman specification test because your data
> does not meet the asymptotic assumptions of the test. The augmented
> regression test is also based on the same assumptions of the Hausman
> and so is of no help in this regard. I am using the Hausman test as
a
> pre-test for two-way causation but cannot perform it in some
> instances. Is any one familiar with any alternatives to theses tests
> for @SLS? If not, is the best approach to report results of both the
> OLS models and the 2SLS models?
>
> Thanks.
> DB
>
>
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Prof. Mark E. Schaffer
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Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS UK
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