非高斯误差下的模型不确定性：随机前沿模型中的贝叶斯模型平均与选择
Model Uncertainty under Non-Gaussian Errors: Bayesian Model Averaging and Selection in Stochastic Frontier Models
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中文总结 AI 辅助
研究非标准随机假设下随机前沿分析中的贝叶斯模型平均与选择，提出快速可靠程序，通过蒙特卡罗模拟比较，发现考虑随机前沿结构会影响后验推断和模型平均结果，尤其在效率分析合理时。
中文摘要 AI 辅助
本文研究非标准随机假设下的贝叶斯模型平均与选择（BMA/S），聚焦于随机前沿分析（SFA）。我们为正态-指数随机前沿模型的推断提出快速、可靠的程序，并检验相对于传统高斯误差BMA/S，考虑非对称干扰是否会影响模型平均和/或选择结果。特别关注SFA应用中典型的适度维协变量选择问题。我们证明，通过适当的搜索策略和并行化技术，穷举模型搜索在计算上是可行的，且在某些情况下比随机搜索更实用。通过蒙特卡罗模拟研究，在不同低效-噪声比和信号强度水平下，将提出的SF-BMA/S程序与标准高斯误差BMA/S进行比较。结果表明，考虑随机前沿结构可能会影响后验推断和模型平均结果，尤其是在效率分析最合理的情况下。
英文摘要
The paper investigates Bayesian Model Averaging and Selection (BMA/S) under non-standard stochastic assumptions, focusing on stochastic frontier analysis (SFA). We propose fast, reliable procedures for inference in the normal-exponential stochastic frontier model and examine whether accounting for asymmetric disturbances affects model averaging and/or selection outcomes relative to the conventional Gaussian-error BMA/S. Particular attention is given to moderate-dimensional covariate selection problems typical in SFA applications. We demonstrate that, with appropriate search strategies and parallelization techniques, exhaustive model search can be computationally feasible and, in some cases, more practical than stochastic search alternatives. A Monte Carlo simulation study is used to compare the proposed SF-BMA/S procedure with standard Gaussian-error BMA/S under varying levels of inefficiency-to-noise ratio and signal strength with respect to the data generating process. The results show that accounting for stochastic frontier structures may affect posterior inference and model averaging outcomes, especially in scenarios where efficiency analysis is most sensible.
