When investigators observe non-random samples from populations, sample selectivity problems may occur. The Heckman selection model is widely used to deal with selectivity problems. Based on the EM algorithm, Zhao et al. (2020) developed three algorithms, namely, ECM, ECM(NR), and ECME(NR), which also have the EM algorithm’s main advantages: stability and ease of imple mentation. This paper provides the implementation of these three new EM-type algorithms in the package EMSS and illustrates the usage of the package on several simulated and real data examples. The comparison between the maximum likelihood estimation method (MLE) and three new EM-type algorithms in robustness issues is further discussed.
Distributions, Econometrics, Finance, Multivariate
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Yang, et al., "The R Journal: EMSS: New EM-type algorithms for the Heckman selection model in R", The R Journal, 2021
BibTeX citation
@article{RJ-2021-098,
author = {Yang, Kexuan and Lee, Sang Kyu and Zhao, Jun and Kim, Hyoung-Moon},
title = {The R Journal: EMSS: New EM-type algorithms for the Heckman selection model in R},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-098},
doi = {10.32614/RJ-2021-098},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {306-320}
}