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Re: st: heteroskedasticity in ordered probit model


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: heteroskedasticity in ordered probit model
Date   Sun, 23 Dec 2012 12:04:15 -0500

Ologit/oprobit models fare a bit better because there is more info in
an ordinal variable. Nonetheless I agree that the appearance of hetero
is often caused by a mis-specified model, e.g important variables are
omitted. There are all sorts of issues with a hetero model (including
the possibility of radically different interpretations of the results,
which the previously mentioned SJ articles also discusses). So, if you
can find a way to not use one, you will often be better off.

Sent from my iPad

On Dec 23, 2012, at 11:31 AM, "JVerkuilen (Gmail)"
<jvverkuilen@gmail.com> wrote:

> Rich Williams' good advice is apropos, but the heteroscedastic probit
> model is kind of fragile and there's an inherent ill-conditioning to
> it. You may well be running it too lean in terms of N. I say this
> speaking as someone who has advocated for the interpretation of
> heteroscedasticity terms in models as often being substantively
> important, so if it's important to you then it may be worth modeling,
> but often heteroscedasticity is "apparent", caused primarily by an
> omitted variable, a poor choice of link function (i.e., maybe you
> should have fit logit or even cauchit instead), or something else.
>
> You may also be better off simply using bootstrapping for your standard errors.
>
> Just some things to think about.
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