Bayesian estimation of stochastic metafrontiers

This paper presents a Bayesian method for estimating a hierarchical panel data stochastic frontier model for metafrontier analysis. It uses Bayesian simulation-based inference and user-friendly software, requiring minimal coding. Applications to simulated and real data confirm the model’s reliability. • Bayesian approach simplifies estimation of hierarchical stochastic metafrontiers. • Simulation in Bayesian framework avoids computationally intensive integration. • Bayesian method ensures reliable estimates with minimal coding effort. • Simulated and real data confirm Bayesian model’s accuracy and applicability. • Provided R and JAGS code facilitates replication and application in case studies.