At the risk of answering something that has already been answered, this
sounds a lot like analysis of covariance: If you can assume the slopes
are the same across the regressions, you have
xi: anova y X i.group where group indicates the regressions. The test
of i.group=0 tests the common intercept. You can also try noconstant to
get the individual intercepts. For non-common slopes you would want
i.group*X in the model
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Jared Delisle
Sent: Wednesday, November 05, 2008 7:41 PM
To: [email protected]
Subject: st: Test of intercepts across OLS regressions
I asked this question a few weeks ago, with no response. So I'll give
it another try and be more explicit, since I was vague in the first
post. Here goes... I have a set of regressions, let's say 10 separate
regressions, all over the same time period, all with the same
specification: Y(n) = a + bX, where n=1..10 (designating which vector of
Y will be the dependent variables), and X is the same vector of
regressors for all regressions. I am interested in testing if the
intercepts, a1...a10, are jointly equal to zero. I can do this with a
-sureg- and -test _cons- which gives me a Wald Chi-square statistic. I
need to perform a Gibbons, Ross, & Shanken (Econometrica 1989) test,
which yields an F-statistic, and is essentially (if I interpret it
correctly) a Hotelling's T-squared statistic times (T-N-1)/[N(T-2)]
where T is the number of time series observations and N is the number of
intercepts (10 in my case). Is anyone familiar with performing such an
analysis? Is there a .do file floating around that calculates this GRS
F-statistic, or any suggestions on how to write one up?
Thanks for your assistance,
Jared
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