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Re: st: hetroscedasticity test after probit


From   Prakash Singh <prakashbhu@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: hetroscedasticity test after probit
Date   Thu, 5 Jul 2012 17:03:22 +0530

Maarten and Yuval

I just saw this paper (Parikh ans Sen 2006, Applied Economics Letters
2006, 13, 699-707) where they have tested the presence of
hetroscedasticity both in probit and hetpro estimators.

Though, I agree with Maarten but I wanted to do this test as the
referee has asked me perform so if there is any way I can test it.


Prakash

On Thu, Jul 5, 2012 at 3:23 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Thu, Jul 5, 2012 at 11:27 AM, Yuval Arbel wrote:
>> Maarten, I believe your implication refers to specification errors in
>> the model, i.e., omission of relevant explanatory variables, leading
>> to biased and inconsistent estimates and predictions. Am I correct?
>
> Not quite, as we defined the probability in terms of the variables in
> our model, so we are by definition not omitting relevant variables.
> The problem is that the probability is only defined within the context
> of the model. A probability is a measure of uncertainty. This does not
> mean that uncertainty is "unexplainable", we can always find
> "explanations" for random events and put those "explanations" into
> variables. However, (hopefully) for substantive reasons we have
> classified these variables as random/unsystematic. (*) Once we have
> made our choice there is, by definition, no omitted variable problem,
> but the difference in variance of the omitted/random/unsystematic
> variables across included co-variates will still cause problems if we
> wish to interpret our results in a causal/counter-factual way.
>
> Another way to think about this is to consider what would happen if we
> could control for everything. In that case there is no uncertainty
> left and the "probabilities" would be either 0 or 1 for all
> observations and the (linear additive) effects could only be -1, 0, or
> 1. This is typically not the kind of estimate of interest.
>
> Hope this helps,
> Maarten
>
> (*) In practice we typically we do so by omission: we choose a set of
> variables to include in our model and define everything else as
> random/unsystematic.
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
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