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Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging

Author

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  • Kamil Makie{l}a
  • B{l}a.zej Mazur
Abstract
Contribution of this paper lies in the formulation and estimation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far. The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of potentially restrictive assumptions embedded within existing SF models. We also develop Bayesian inference methods that are less restrictive compared to the ones used so far and demonstrate feasible approximate alternatives based on maximum likelihood.

Suggested Citation

  • Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    • Handle: RePEc:arx:papers:2003.07150
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    References listed on IDEAS

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    Cited by:

    1. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.

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