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Re: st: Heteroskedasticity Test in svy
From
R G <[email protected]>
To
[email protected]
Subject
Re: st: Heteroskedasticity Test in svy
Date
Thu, 17 Mar 2011 08:43:07 -0700 (PDT)
I don't mean to press the issue on it, but this is my first analysis using the
svy commands. Why is it that the F-test wouldn't be informative? Is it something
general for svy: reg, or is it particular to my data/analysis? I don't think
I've ever seen this message come up using Stata before.
Robin
----- Original Message ----
From: Nick Cox <[email protected]>
To: "[email protected]" <[email protected]>
Sent: Thu, March 17, 2011 11:28:27 AM
Subject: RE: st: Heteroskedasticity Test in svy
This is a kind of a curious argument here.
Stata is saying, as I understand it, "We are not going to show you this, because
we don't think it makes much sense." That wouldn't be the end of the story if
you had better technical reasons why StataCorp are wrong, which certainly does
happen.
But wanting a number just to placate the audience....
If I am ever in the audience, can I have an opt out?
(intended seriously, but not aggressively)
Nick
[email protected]
Robin Hertner
Does this mean then I don't need to test for heterskedasticity at all when
runing svy: reg? I'm comparing different models - my normal diagnostic run
through apart from t-tests of the coefficients, R2, LR tests/Wald tests, and
normality of residuals, is to look at remaining heteroskedasticity.
As an aside, in running svy: reg on my models, the model's F-test is blank.
Stata gives the explanation that this isn't a problem, it's just not computed.
Is there a way that I can compute this other than manually? I'm not worried
about it, but it's one of those stats that conferences/publications likes to
see.
From: Stas Kolenikov <[email protected]>
On Thu, Mar 17, 2011 at 8:18 AM, R G <[email protected]> wrote:
> I'm using Stata's svy commands, and am trying to figure out if there is an
> equivalent to imtest, White to do a heteroskedasticity test (besides plotting
> residuals and variables).
It does not make sense in the context of design-based inference
paradigm. There are no information matrices to talk about, and
variance estimators are generalizations of White's
heteroskedasticity-robust estimators, i.e., correct for
heteroskedasticity already. If you want to pursue small gains in
efficiency by modeling variance of the residuals, you can try to do
that, but you run into risk of misspecifying the variance model which
would only make things worse.
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