Bayesian estimation of stochastic metafrontiers
提出一种贝叶斯方法估计层次面板数据随机前沿模型，用于元前沿分析，通过模拟和真实数据验证了可靠性，适合需要最小化编程的实证研究者。
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.