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Community,

I am running a left- and right-censored tobit regression model. The dependent variable is the proportion of cash used in M&A transactions running from 0 to 1.

I assume heteroskedasticity to be prevalent due to the characteristics of my cross-sectional sample as well as the BPCW test for the LS regression model. In order to test the tobit specifications, I used bctobit. However, bctobit is not applicable for right-censored data.

This gives rise to the following question: - Is there another user-written command to test for the tobit specifications with right- and left-censored data?

Thanks a lot for your efforts!

Michael Kuehne
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The most immediate question to me here is statistical. From what you say this approach is inadvisable, so how to implement it is immaterial.

I don't think Tobit makes much sense for variables that are defined to lie in an interval. Censoring to me implies that some high or low values might have been observed in principle, but in practice are recorded as less extreme values. It seems to me that logit or probit are the appropriate link functions for proportional responses, and in that Stata that means glm with e.g. logit link.

Regardless of that, do you regard linear dependence as expected here?

For an excellent concise review making this point, see http://www.stata-journal.com/sjpdf.html?articlenum=st0147

Nick Cox
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  • I highly appreciate your hints, Mr Cox. The attached link was fairly helpful. However, a few questions remain unanswered. I suppose I did not fully understand your answer. The dependence is supposed to be linear. Indeed, I do understand that logit is appropriate for binary dependent variables. However, I do not fully understand why tobit is "less appropriate" for modeling a continuous binary variable ranging from 0 to 1. – Michael Kuehne May 09 '13 at 12:28
  • I don't know how to explain it more clearly, but 1. You don't have censoring. 2. Linear functional forms seems quite implausible as the response tends to either 0 or 1. – Nick Cox May 09 '13 at 19:02
  • In the paper that Nick linked there's a explantion of why the tobit is inappropriate: "Some researchers have considered using censored normal regression techniques such as tobit on proportions data that contain zeros or ones. However, this is not an appropriate strategy, as the observed data in this case are not censored: values outside the [0,1] interval are not feasible for proportions data." – dimitriy May 09 '13 at 19:55
  • Thanks a lot for your comments. Further research by myself proved your comments and I am indeed quite happy about it. I adjusted my models and performed both logistic and glm regression. The results are actually how I expected it to be. Thanks a lot! – Michael Kuehne May 10 '13 at 10:01
  • Good. Thanks for closure here. (Too many posters here never acknowledge or comment on replies.) – Nick Cox May 10 '13 at 11:16