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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Outlier diagnostics for tobit (postestimation) |

Date |
Fri, 19 Oct 2012 11:00:13 +0100 |

My #3 was 3. You should be clear that you really do have a tobit problem and not one analogous to a logit or probit problem. We see on this list examples in which -tobit- or -intreg- is being applied to problems in which responses outside an interval [a, b] are impossible in principle. The arguments from at least some of us are that that kind of problem is usually much better treated as a logit or probit or beta regression problem. (Clearly any [a, b] can be scaled to [0, 1].) You are repeating your question on whether you can pretend that your problem is a standard regression problem and want to be told "right or wrong". Who can tell? Perhaps it would be a decent approximation; perhaps it would be lousy. We don't have your data to try it out. But my view is that if you have a bounded response, you should be respectful of those bounds in all your analysis. I wouldn't be impressed at that kind of treatment if I were a reviewer of your paper (examiner of your thesis, whatever). Nick (I am deliberately avoiding "censored" or "truncated" as terms of art, partly because of the first point I made.) On Fri, Oct 19, 2012 at 10:48 AM, Timo Beck <timoworldwide@gmx.de> wrote: > Dear Nick and Jay, > > Thank you for your help. > > @ Nick: I already checked cases for clear outliers, e.g., implausible values (and also simulated different versions). Further I used logarithmic transformation for specific variables which also helped. Still I wanted to use some "established" method for a further check (not for the main analysis, but rather as a robustness check). Not sure, what you mean by number 3) though. > > @ Jay: Thank you for the hint, I will definitely look into that. > > Once again quickly re my other question, maybe you also have an opinion on whether, just as a robustness test, I could fit OLS as an approximation of the tobit model and use outlier diagnostics thereafter and then simulate the tobit without these identified cases? Or would I be doing something completely wrong? According to Wooldridge a linear model is a good approximation for E(y) in a corner solution model which is what I am looking at. That's why I am thinking that way. * * 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/

**References**:**Re: st: Outlier diagnostics for tobit (postestimation)***From:*"Timo Beck" <timoworldwide@gmx.de>

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