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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). * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/