In this paper, we propose a copula ML estimation method for the sample selection model
using the Ali-Mikhail-Haq (AMH) copula. The proposed AMH copula ML estimation is
compared with the well-known bivariate ML estimation and Heckman’s two-step
estimation. Monte Carlo experiments are conducted to compare their performance in terms
of the mean squared error (MSE) depending on the following 2 conditions: (i) whether the
imposed distributional assumption is correct, and (ii) whether some regressors of the
participation and outcome equation are correlated. The results of the experiments show that
the estimation results for the proposed method can be better than those of the two wellknown
methods, particularly when the imposed distributional assumption is incorrect and
some regressors of the two equations are correlated. Hence, the proposed method can be a
practically useful alternative for the sample selection model.