Influence diagnostics in the Heckman selection models based on EM algorithms

• Published in: Journal of applied statistics Vol. 52; no. 13; pp. 2384 - 2412
• Main Authors: Oliveira, Marcos S., Prates, Marcos O., Galarza, Christian E., Lachos, Victor H.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 03.10.2025; Taylor & Francis Ltd
• Subjects: Algorithms; Bivariate analysis; Case-deletion; Heckman selection model; local influence; model perturbation; multivariate Student's-t; Case-deletion; Heckman selection model; local influence; model perturbation; multivariate Student's-t
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2025.2461715

This study presents diagnostic techniques for Heckman selection models estimated using the EM algorithm. The focus is on the selection t and normal models, based on the bivariate Student's-t and bivariate normal distributions, respectively. The Heckman selection model is a key econometric tool for estimating relationships while addressing selection bias. Relying on the EM-type algorithm, we develop global and local influence analyses based on the conditional expectation of the complete-data log-likelihood function, exploring four perturbation schemes for local influence analysis. To assess the effectiveness of the proposed diagnostic measures in identifying influential observations, we conducted a simulation study, complemented by two real-data applications that demonstrate how these techniques can effectively identify influential points. The proposed algorithms and methodologies are incorporated into the R package HeckmanEM .