The R Journal: accepted article

This article will be copy edited and may be changed before publication.

EMSS: New EM-type algorithms for the Heckman selection model in R PDF download
Kexuan Yang, Sang Kyu Lee, Jun Zhao and Hyoung-Moon Kim

Abstract 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 EM algorithm, Zhao et al. (2020) developed three algorithms, namely, ECM, ECM(NR), and ECME(NR), which also have EM algorithm’s main advantages: stability and ease of implementation. 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 compari son between maximum likelihood estimation method (MLE) and three new EM-type algorithms in robustness issues is further discussed.

Received: 2020-10-30; online 2021-12-15
CRAN packages: sampleSelection, mvtnorm
CRAN Task Views implied by cited CRAN packages: Distributions, Econometrics, Finance, Multivariate


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-098,
  author = {Kexuan Yang and Sang Kyu Lee and Jun Zhao and Hyoung-Moon
          Kim},
  title = {{EMSS: New EM-type algorithms for the Heckman selection model
          in R}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-098},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-098/index.html}
}