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New EM-type algorithms for the Heckman selection model

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

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.

Suggested Citation

  • Zhao, Jun & Kim, Hea-Jung & Kim, Hyoung-Moon, 2020. "New EM-type algorithms for the Heckman selection model," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:csdana:v:146:y:2020:i:c:s0167947320300219
    DOI: 10.1016/j.csda.2020.106930
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    References listed on IDEAS

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    Cited by:

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    4. Kulindwa, Yusuph J. & Ahlgren, Erik O., 2021. "Households and tree-planting for wood energy production – Do perceptions matter?," Forest Policy and Economics, Elsevier, vol. 130(C).

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