  Heckman selection model is perhaps the most popular econometric model in the
analysis of data with sample selection. The analyses of this model are based on
the normality assumption for the error terms, however, in some applications,
the distribution of the error term departs significantly from normality, for
instance, in the presence of heavy tails and/or atypical observation. In this
paper, we explore the Heckman selection-t model where the random errors follow
a bivariate Student's-t distribution. We develop an analytically tractable and
efficient EM-type algorithm for iteratively computing maximum likelihood
estimates of the parameters, with standard errors as a by-product. The
algorithm has closed-form expressions at the E-step, that rely on formulas for
the mean and variance of the truncated Student's-t distributions. Simulations
studies show the vulnerability of the Heckman selection-normal model, as well
as the robustness aspects of the Heckman selection-t model. Two real examples
are analyzed, illustrating the usefulness of the proposed methods. The proposed
algorithms and methods are implemented in the new R package HeckmanEM.
