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Influence diagnostics in the Heckman selection models based on EM algorithms.

Marcos S Oliveira1, Marcos O Prates2, Christian E Galarza3

  • 1Department of Mathematics and Statistics, Federal University of Sao Joao Del Rei, Sao Joao Del Rei, MG, Brazil.

Journal of Applied Statistics
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces new diagnostic methods for Heckman selection models, crucial for econometrics. These techniques, implemented in the HeckmanEM R package, effectively identify influential data points in statistical analyses.

Keywords:
Case-deletionHeckman selection modellocal influencemodel perturbationmultivariate Student's-t

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Heckman selection models are vital for addressing selection bias in statistical estimations.
  • Existing diagnostic techniques may not fully capture influential observations in these models.

Purpose of the Study:

  • To develop novel diagnostic techniques for Heckman selection models estimated via the EM algorithm.
  • To provide robust methods for identifying influential observations in selection t and normal models.

Main Methods:

  • Utilized the EM algorithm for estimating Heckman selection models.
  • Developed global and local influence analyses based on the complete-data log-likelihood.
  • Explored four perturbation schemes for local influence analysis.

Main Results:

  • The proposed diagnostic measures effectively identify influential observations.
  • Simulation studies and real-data applications validated the methodology.
  • The R package HeckmanEM incorporates the developed algorithms and diagnostics.

Conclusions:

  • The new diagnostic techniques enhance the reliability of Heckman selection model estimations.
  • The HeckmanEM R package provides a practical tool for applied researchers.
  • Accurate identification of influential points is critical for robust econometric analysis.