Goodness-of-fit in production models: A Bayesian perspective
提出一种融合非参数活动分析与随机前沿模型的新方法，允许每个投入和产出有各自的低效率和噪声，通过贝叶斯压缩和人工神经网络解决参数过多问题，并纳入环境变量，用美国银行数据验证。
We propose a general approach for modeling production technologies, allowing for modeling both inefficiency and noise that are specific for each input and each output. The approach is based on amalgamating ideas from nonparametric activity analysis models for production and consumption theory with stochastic frontier models. We do this by effectively re-interpreting the activity analysis models as simultaneous equations models in Bayesian compression and artificial neural networks framework. We make minimal assumptions about noise in the data and we allow for flexible approximations to input- and output-specific slacks. We use compression to solve the problem of an exceeding number of parameters in general production technologies and also incorporate environmental variables in the estimation. We also present Monte Carlo simulation results and an empirical illustration of this approach for US banking data.