@article{Hasebe:249807,
      recid = {249807},
      author = {Hasebe, Takuya},
      title = {Copula-based maximum-likelihood estimation of  sample-selection models},
      journal = {Stata Journal},
      address = {2013},
      number = {199-2016-2864},
      pages = {29},
      year = {2013},
      abstract = {Sample-selection issues are common problems in empirical  studies of labor economics and other applied  microeconomics. A common estimation method is maximum  likelihood estimation under the assumption of joint  normality. It is well known, however, that the violation of  distributional assumptions leads to inconsistency of a  maximum likelihood estimator. Early work on  sample-selection models that relaxes the normality  assumption was done by Lee (1983, 1984). His approach was  to transform nonnormal disturbances in the models into  normal variates that are then assumed to be jointly  normally distributed. As we will see, this is a special  case of the copula approach that Smith (2003) applies to  sample-selection models. The copula approach adds more  flexibility to model specifications.

In this article, I  discuss the maximum likelihood estimation of  sample-selection models with the copula approach to relax  the assumption of joint normality. Although there are  several types of sample-selection models, I discuss two in  particular: a bivariate sample-selection model and an  endogenous switching regression model. I also introduce the  Stata commands heckmancopula and switchcopula, which  implement the estimation of each model, respectively.},
      url = {http://ageconsearch.umn.edu/record/249807},
      doi = {https://doi.org/10.22004/ag.econ.249807},
}