New EM-type algorithms for the Heckman selection model

Jun Zhao, Hea-Jung Kim and Hyoung-Moon Kim

Computational Statistics & Data Analysis, 2020, vol. 146, issue C

Abstract: The Heckman selection model is widely used to analyse data for which the outcome is partially observable, and the missing part is not random. The 2-step method, maximum likelihood estimation (MLE), and EM algorithms have been developed to analyse this model; however, they have certain limitations. Three new algorithms (ECM, ECM(NR), and ECME) will be proposed with the advantages of the EM algorithm: easy implementation and numerical stability. Considering bias and mean squared error (MSE), simulations with different correlation values suggest that MLE performs similarly to the proposed algorithms; however, MLE as well as the proposed algorithms yield better estimations than the 2-step method. A simulation study in which standard error is also considered demonstrates that the new algorithms are more robust than MLE, and yield slightly better estimations than the 2-step and the robust 2-stage methods. Real data analyses are also provided to discuss the performance of MLE, 2-step, and the proposed algorithms. A real data analysis concerning the robustness issue further illustrates that, under certain conditions, the proposed algorithms are more efficient and stable.

Keywords: Heckman model; Skew-normal; EM algorithm; Robustness (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947320300219
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:146:y:2020:i:c:s0167947320300219

DOI: 10.1016/j.csda.2020.106930

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().