Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis
Kamil Makieła () and
Błażej Mazur ()
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Błażej Mazur: Department of Empirical Analyses of Economic Stability, Cracow University of Economics, Rakowicka 27, 31-510 Krakow, Poland
Econometrics, 2020, vol. 8, issue 2, 1-22
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.
Keywords: stochastic frontier analysis; Bayesian model averaging; Bayesian inference; model uncertainty; efficiency analysis; public policy (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:2:p:13-:d:347990
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