Richard,
Thanks for your detailed responses. I guess I am having trouble
translating my intuition from linear models. My understanding is
that the use of robust in a linear context results in an uu' matrix
in which the diagonal elements are potentially different, i.e. the
error term for each observation is drawn from a normal distribution
with mean zero and a variance that may differ from observation to
observation. In oprobit w/robust (leave aside cluster), what does it
mean to say that all of the u_j's are normal(0,1) but, when it comes
to calculating standard errors for parameter estimates, the diagonal
elements of the uu'are potentially different from one another?
Thanks again,
Matt
--- Richard Williams <Richard.A.Williams.5@ND.edu> wrote:
> At 06:55 PM 6/1/2006, Matt Barmack wrote:
>>Specifying cluster or robust does not seem to change the predicted
>>probabilities from oprobit. Does it? Shouldn't it?
>>Intuitively/naively, I am thinking that for an observation for
> which
>>the variance of the random part of the latent index is high, there
> is
>>a greater chance of ending up further away from what the
>>deterministic part of the latent index alone might suggest.
>
> One other clarification: by "variance of the random part of the
> latent index" I assume you mean the residual. In Probit the
> residual
> is assumed to have a Normal(0, 1) distribution. The linear
> prediction is an estimate and is subject to sampling variability.
> I
> suppose if one were so inclined, you could compute the confidence
> interval for that estimate, and then based on that estimate come up
>
> with a range for the predicted probabilities. I don't recall ever
> having seen that done. Nor am I exactly sure how you would do it,
> since the estimates of the cutpoints are themselves subject to
> sampling variability.
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
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>
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