For stochastic frontier models, an exhaustive pre-screened Bayesian model search is feasible up to about 30 candidates, and non-Gaussian errors matter most when inefficiency dominates noise and signal strength is weak.
On the effect of prior assumptions in Bayesian model averaging with applications to growth regression
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Model Uncertainty under Non-Gaussian Errors: Bayesian Model Averaging and Selection in Stochastic Frontier Models
For stochastic frontier models, an exhaustive pre-screened Bayesian model search is feasible up to about 30 candidates, and non-Gaussian errors matter most when inefficiency dominates noise and signal strength is weak.