论文标题
随机前沿分析具有广义错误：推理，模型比较和平均
Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging
论文作者
论文摘要
本文的贡献在于对随机前沿分析（SFA）的一般模型的制定和估计，该模型几乎嵌套了所有使用的形式，并且包括到目前为止尚未考虑的一些形式。该模型基于观测误差的广义t分布和与效率相关的项的第二种beta分布。我们使用此通用错误结构框架进行正式测试，比较替代规格并进行平均模型。这使我们能够处理模型规范的不确定性，这是SFA中未解决的主要问题之一，并放宽了现有SF模型中嵌入的许多潜在限制性假设。我们还开发了与迄今为止使用的推理相比，这些推理方法的限制性较小，并根据最大似然表现出可行的近似替代方法。
Contribution of this paper lies in the formulation and estimation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far. The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of potentially restrictive assumptions embedded within existing SF models. We also develop Bayesian inference methods that are less restrictive compared to the ones used so far and demonstrate feasible approximate alternatives based on maximum likelihood.
