Semiparametric Bayesian inference for stochastic frontier models
随机前沿模型的半参数贝叶斯推断
In this paper we propose a semiparametric Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The distribution of inefficiencies is modelled nonparametrically through a Dirichlet process prior. We suggest prior distributions and implement a Bayesian analysis through an efficient Markov chain Monte Carlo sampler, which allows us to deal with practically relevant sample sizes. We also allow for the efficiency distribution to vary with firm…