Re: st: heteroskedasticity in ordered probit model

["JVerkuilen (Gmail)" <jvverkuilen@gmail.com>]

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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