Abstract
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.