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


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/


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