Joint production in stochastic non-parametric envelopment of data with firm-specific directions
提出一种基于似然的随机非参数数据包络估计方法，使用企业特定方向向量估计企业效率，并通过贝叶斯自助法增强模型稳健性，蒙特卡洛实验和银行数据验证了其有效性。
We propose a likelihood-based approach to Stochastic Non-Parametric Envelopment of Data (StoNED) estimator using a directional distance function with firm-specific directional vectors. Additionally, we show how to estimate firm-specific inefficiency estimates instead of focusing on their average only. Moreover, we propose models that are robust to misspecification in general and the use of unit-information-priors in this class of models. These priors control the amount of information to be exactly equal to one observation. In this context, we propose the use of Bayesian Bootstrapping to further mitigate possible misspecification. We also propose empirical tests for identification of the model. Monte Carlo experiments show the good performance of the new techniques and an empirical application to the technology of large U.S. banks shows the feasibility of the new techniques.