🤖 AI Summary
This study addresses covariate selection and model uncertainty in stochastic frontier models under non-Gaussian errors by proposing an efficient inference framework based on Bayesian model averaging and selection. Leveraging parallelized exhaustive search, Monte Carlo simulation, and a normal-exponential stochastic frontier specification, the paper systematically evaluates the impact of asymmetric disturbances on posterior inference. The findings demonstrate that, in moderate-dimensional covariate settings, a well-designed exhaustive search strategy outperforms random search. Moreover, explicitly modeling the stochastic frontier structure significantly enhances the robustness and accuracy of model-averaged estimates across varying efficiency-to-noise ratios and signal strengths.
📝 Abstract
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.