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Re: st: Outlier diagnostics for tobit (postestimation)


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Outlier diagnostics for tobit (postestimation)
Date   Thu, 18 Oct 2012 20:56:05 +0100

This is very similar to questions asked within the last week in
various threads started by Ebru Ozturk.

One short answer is

1. Not much is obviously available, as the idea of residuals is
problematic if the response is bounded by definition.

2. You can always try simulating to see what would happen with what
you regard as a reasonable data generation process.

3. You should be clear that you really do have a tobit problem and not
one analogous to a logit or probit problem.

4. If you are worried about outliers in your predictors you can always
consider applying an appropriate transformation.

All that said, your posting violates a firm and explicit Statalist
request to use your full real name. See 2.1.3 at

http://www.stata.com/support/faqs/resources/statalist-faq/#tojoin

On Thu, Oct 18, 2012 at 8:44 PM,  <timoworldwide@gmx.de> wrote:

> I hope someone can help me with the following question on regression diagnostics for tobit. So far I've only used regress and for outlier diagnostics normally cooksd, rstudent and dfbeta. As these are not available for tobit postestimation I wondered if anything comparable exists for tobit that I could use (and have not found so far). As I deal with a two-limit tobit, I am mainly interested in outliers of the independent variables (i.e., cooksd and dfbeta).
>
> If nothing exists, would it be possible (only for regression diagnostic purposes) to just fit OLS and use postestimation tools thereafter instead of after tobit?
>
> Due to the large dataset graphical solutions do not work. I use Stata 10 (Stata/SE 10.1 for Windows, Born 01 Oct 2009).

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