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Open AccessArticle

Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis

1
Department of Econometrics and Operational Research, Cracow University of Economics, Rakowicka 27, 31-510 Krakow, Poland
2
Department of Empirical Analyses of Economic Stability, Cracow University of Economics, Rakowicka 27, 31-510 Krakow, Poland
*
Author to whom correspondence should be addressed.
Econometrics 2020, 8(2), 13; https://doi.org/10.3390/econometrics8020013
Received: 13 December 2019 / Revised: 17 March 2020 / Accepted: 14 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
This paper discusses Bayesian model averaging (BMA) in Stochastic Frontier Analysis and investigates inference sensitivity to prior assumptions made about the scale parameter of (in)efficiency. We turn our attention to the “standard” prior specifications for the popular normal-half-normal and normal-exponential models. To facilitate formal model comparison, we propose a model that nests both sampling models and generalizes the symmetric term of the compound error. Within this setup it is possible to develop coherent priors for model parameters in an explicit way. We analyze sensitivity of different prior specifications on the aforementioned scale parameter with respect to posterior characteristics of technology, stochastic parameters, latent variables and—especially—the models’ posterior probabilities, which are crucial for adequate inference pooling. We find that using incoherent priors on the scale parameter of inefficiency has (i) virtually no impact on the technology parameters; (ii) some impact on inference about the stochastic parameters and latent variables and (iii) substantial impact on marginal data densities, which are crucial in BMA. View Full-Text
Keywords: stochastic frontier analysis; Bayesian model averaging; Bayesian inference; model uncertainty; efficiency analysis; public policy stochastic frontier analysis; Bayesian model averaging; Bayesian inference; model uncertainty; efficiency analysis; public policy
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Makieła, K.; Mazur, B. Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis. Econometrics 2020, 8, 13.

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