Bayesian learning in performance. Is there any?
本文提出并实现了一个绩效的贝叶斯学习模型，利用随机前沿模型分析技术效率，并应用于美国大型银行数据，发现技术效率中存在一定学习效应，但经验跳跃与生产率增长关系有限。
We propose and implement a Bayesian learning model for performance. The model implies a specific distribution for performance / technical inefficiency which we exploit in the context of stochastic frontier models. As the theoretical model is ambiguous with respect to what constitutes existing “experience”, we propose and implement alternative specifications. The estimation and inference techniques are based on Bayesian analysis using Markov Chain Monte Carlo methods. We apply the new techniques to a data set of large U.S. banks. Our findings indicate that there is some learning in technical inefficiency although there is limited evidence, if at all, that jumps in experience are related to productivity growth. However, this effect is distinctly pronounced for the 2007–2010 period but much less significant afterwards.