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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 08:14:00 -0700 (PDT)
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.
robin
----- Original Message ----
From: Stas Kolenikov <[email protected]>
To: [email protected]
Sent: Thu, March 17, 2011 10:45:10 AM
Subject: Re: st: Heteroskedasticity Test in svy
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.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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