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From |
Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Using a Tobit regression with the Heckman correction |

Date |
Sat, 17 Dec 2011 14:46:14 -0800 |

You are looking for the truncated normal hurdle model, also called the Craggit model. It has been programmed up in Stata by William Burke (Stata Journal, Volume 9, Number 4): http://www.stata-journal.com/article.html?article=st0179 To install the command in Stata, type: net install st0179, /// from(http://www.stata-journal.com/software/sj9-4) T On Sat, Dec 17, 2011 at 2:17 PM, J. Boreham <jb648@cam.ac.uk> wrote: > Thanks for your prompt response, and for the clarification. > > I think I need to take both types of Tobit into account. The "fee" variable > is the transfer fee paid for footballers, so is only observed when > "transferred" is equal to 1 - so there is a Type II Tobit. However, in > addition, "fee" is always greater than or equal to 0, so there is a Type I > Tobit too. > > Am I right in thinking that the -heckman- command will account for the Type > II Tobit, but not the Type I Tobit? I attempted to account for both by using > the -tobit- command (to account for the Type I), and also including the > inverse Mills ratio ("mills_ratio" below, to account for the Type II). I > fear, however, that this will result in invalid standard errors, as the > Heckman correction requires non-standard errors (as "the usual formulas for > standard errors for least squares coefficients are not appropriate" - > Heckman, Sample Selection Bias as a Specification Error, 1979) > > > Thanks again for your time, > > John > > > > > On Dec 17 2011, Tirthankar Chakravarty wrote: > >> I think you might be mixing up a few things here. Both -heckman- and >> -tobit- fit Tobit (censored regression) models, i.e., where the >> outcome of interest is not fully observed in the sample. They differ >> in what they posit the censoring mechanism to be. >> >> 1) The model fitted by -tobit- is what is called the Type I tobit. >> Here the observability of the outcome depends on the values of the >> outcome itself - whether it crosses a non-stochastic threshold. >> >> 2) The model fitted by -heckman- is what is called the Type II tobit. >> Here the observability of the outcome (your "fee" variable) depends on >> the values of a binary indicator (probably what your "transferred" >> variable refers to), which is modelled using a probit regression. >> Conditional on the values of the binary indicator the second stage is >> a simple linear regression fitted by OLS - where the conditionality is >> taken into account using the Mills ratio. >> >> There is also the possibility of fitting the whole model in one go >> using partial maximum likelihood, but the important point is that the >> conditional model for the outcome is a linear regression, and in the >> two-step version of Heckman's estimator, the outcome is fitted using >> OLS. >> >> Lastly, note that OLS is an estimation technique and tobit is a model. >> >> T >> >> On Sat, Dec 17, 2011 at 1:16 PM, J. Boreham <jb648@cam.ac.uk> wrote: >>> >>> Dear Statalist, >>> >>> I'm very new to Stata, so apologise if this is a silly question. I'm >>> looking to run a Tobit regression using the Heckman correction. My Heckman >>> code is: >>> >>> ***** heckman fee age agesq curr_app curr_goal_d curr_goal_m curr_goal_f, >>> /// select(transferred = age agesq curr_app curr_goal_d curr_goal_m >>> curr_goal_f prom) twostep ***** >>> >>> But this uses OLS rather than a Tobit model. I instead attempted to >>> create the two stage Heckman correction by first manually producing the >>> inverse Mills ratio, and then running a Tobit regression: >>> >>> ***** probit transferred age agesq curr_app curr_goal_d curr_goal_m >>> curr_goal_f prom >>> >>> predict predicted_values, xb >>> >>> generate denominator = normal(predicted_values) >>> generate numerator = normalden(predicted_values) >>> generate mills_ratio = numerator/denominator >>> >>> tobit fee age agesq curr_app curr_goal_d curr_goal_m curr_goal_f >>> mills_ratio ***** >>> >>> However, this will not account for the non-standard errors one needs when >>> using the Heckman correction. >>> >>> So is it possible either to tell Stata to use a Tobit regression with the >>> "heckman" command, or instead to get the correct standard errors when >>> manually doing the correction by inserting the inverse Mills ratio? >>> >>> >>> Thanks for your consideration, >>> >>> John Boreham >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >> >> >> >> >> > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- Tirthankar Chakravarty tchakravarty@ucsd.edu tirthankar.chakravarty@gmail.com * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Using a Tobit regression with the Heckman correction***From:*"J. Boreham" <jb648@cam.ac.uk>

**References**:**st: Using a Tobit regression with the Heckman correction***From:*"J. Boreham" <jb648@cam.ac.uk>

**Re: st: Using a Tobit regression with the Heckman correction***From:*Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>

**Re: st: Using a Tobit regression with the Heckman correction***From:*"J. Boreham" <jb648@cam.ac.uk>

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