Robust Bayesian Inference in Stochastic Frontier Models
Lancaster University Management School, Lancaster University, Lancaster LA1 4YX, UK
J. Risk Financial Manag. 2019, 12(4), 183; https://doi.org/10.3390/jrfm12040183
Received: 4 November 2019 / Revised: 2 December 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Advances in Econometric Analysis and Its Applications)
We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, and are robust to changes in the model. Specifically, we examine robustness to changes in the distribution of the composed error in the stochastic frontier model (SFM). Moreover, coarsening is a form of regularization, reduces overfitting and makes inferences less sensitive to model choice. The new techniques are illustrated using artificial data as well as in a substantive application to large U.S. banks.
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MDPI and ACS Style
Tsionas, M.G. Robust Bayesian Inference in Stochastic Frontier Models. J. Risk Financial Manag. 2019, 12, 183.
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