Aren't things a little trickier in the oprobit and similar contexts?
Suppose that you are interested in the prediction in the sense of the
category that the model suggests has the highest probability. If the
variance of the linear index is higher, there will be higher
probability associated with "further away" categories potentially
changing the category that the model predicts to be most likely. I
apologize for the foregoing abuse of formal statistical terms.
On a related note, does Stata have anthing similar to SAS's qlim that
allows the user to specify that the variance of the latent index is a
function of one or several variables?
--- Nick Winter <[email protected]> wrote:
> Larger standard errors make it more likely you are further away
> from
> the deterministic part--yes--but you could be higher, or you could
> be
> lower. Your best guess--and therefore your prediction--is
> unchanged.
>
> --NW
>
> At 09:14 AM 6/2/2006, you 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.
>
> ________________________________________________________
> Nicholas J. G. Winter 607.255.8819 t
> Assistant Professor 607.255.4530 f
> Department of Government [email protected] e
> Cornell University falcon.arts.cornell.edu/nw53 w
> 308 White Hall
> Ithaca, NY 14853-4601
>
>
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