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- Title
Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis.
- Authors
Makieła, Kamil; Mazur, Błażej
- Abstract
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.
- Subjects
STOCHASTIC analysis; STOCHASTIC frontier analysis; LATENT variables; ARITHMETIC mean; PROBABILITY theory
- Publication
Econometrics (2225-1146), 2020, Vol 8, Issue 2, p13
- ISSN
2225-1146
- Publication type
Article
- DOI
10.3390/econometrics8020013