Re: st: MLE of Heckman model with endogeneity

[Stas Kolenikov <skolenik@gmail.com>]

You said that your Y2 that you were interested in was hours, a
continuous rather than a binary variable... and you did not say yours
were panel data. With -cmp-, you can introduce random effects,
although I am not sure how reasonable they would be given the nature
of your problem, and economists do not like the random effects very
much.


-- Stas Kolenikov, PhD, PStat (ASA, SSC)
-- Principal Survey Scientist, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
-- http://stas.kolenikov.name



On Wed, Feb 12, 2014 at 7:39 PM, Nina Parfinenko <nparfin@gmail.com> wrote:
> Hi Stas,
>
> Thank you for your answer. This is a snippet of my data:
>
> id employed(Y2) logwage(Y1) experience(X,Z) part-time-work(X)
> non-labor-income(Z)
> 100002 Employed 259.1516 21 1 10.38887
> 100002 Employed 222.4555 21.91667 1 10.7015
> 100002 Employed 252.914 22.88889 1 11.11234
> 100002 Employed 274.1406 23.80556 1 10.79897
> 100004 Employed 241.0749 11 1 10.58269
> 100004 Employed 222.7311 12.02778 1 10.95263
> 100004 Employed 382.0812 12.58333 1 10.72274
> 100004 Employed 244.6271 13.55556 1 11.40245
> 100005 Employed 173.5379 0.9444444 1 8.768297
> 100005 Employed 251.9188 2.083333 1 9.007585
> 100005 Employed 229.8504 3.027778 1 0
> 100005 Not employed . 5.000381 0 10.37486
> 100005 Not employed . 5.000381 0 9.950747
> 100005 Not employed . 5.000381 0 10.39137
> 100005 Not employed . 5.000381 0 10.3661
> 100005 Not employed . 5.000381 0 10.11485
>
> This is panel data of females, with 25 variables in both X & Z, 5 only
> in X and 9 only in Z. Out of 39 variables, 31 are dummies. Variables
> include age, experience, tenure, marital status, non-labor income,
> education, children, enlish-speaking background, occupation, health
> and ethnicity.
>
> Thanks for the pointer to -cmp-, it seems to cover both -heckman- and
> -ivprobit-, so maybe it can be used for their hybrid. On the other
> hand, I suspect my ML function could be non-concave.
> Best,
> Nina
>
> On Wed, Feb 12, 2014 at 3:29 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
>> Ninochka,
>>
>> can you give a snippet of your data, like the first ten observations
>> with a single x, single z, and all the relevant patterns of what is
>> observed?
>>
>> I think David Roodman's -cmp- should be able to handle it, see
>> http://www.stata-journal.com/article.html?article=st0224.
>>
>>
>> -- Stas Kolenikov, PhD, PStat (ASA, SSC)
>> -- Principal Survey Scientist, Abt SRBI
>> -- Opinions stated in this email are mine only, and do not reflect the
>> position of my employer
>> -- http://stas.kolenikov.name
>>
>>
>>
>> On Tue, Feb 11, 2014 at 8:17 AM, Nina Parfinenko <nparfin@gmail.com> wrote:
>>> Dear Statalist,
>>>
>>> I need to estimate a two-equation sample selection model for wage (y1)
>>> and hours (y2). y2 is not observed, only whether y2>0 or y2=0. Wage y1
>>> is observed only if y2>0. Also y1 is endogenous in the y2 equation.
>>> More precisely, the two equations are
>>>
>>> y1=betaX+e1
>>> y2*=alpha*y1+gammaZ+e2
>>>
>>> and (e1, e2) are jointly normal.
>>>
>>> This model could be estimated with ivprobit if y1 were always
>>> observed. On the other hand, it would be a candidate for heckman
>>> command if not for the endogeneity of y1 in the y2 equation. I seems
>>> that Stata ml commands do not take endogenous variables either.
>>>
>>> Is it possible to estimate the above model with maximum likelihood
>>> (the 2 equations simultaneously) in Stata using built-in commands? I
>>> have already estimated the system with a two-step procedure and now
>>> need to estimate it jointly and simulatenously with Maximum
>>> Likelihood.
>>> Many thanks,
>>> Nina
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