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Robust Bayesian Inference in Stochastic Frontier Models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Robust Bayesian Inference in Stochastic Frontier Models. / Tsionas, Mike G.
In: Journal of Risk and Financial Management, 04.12.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, MG 2019, 'Robust Bayesian Inference in Stochastic Frontier Models', Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm12040183

APA

Tsionas, M. G. (2019). Robust Bayesian Inference in Stochastic Frontier Models. Journal of Risk and Financial Management, Article 183. https://doi.org/10.3390/jrfm12040183

Vancouver

Tsionas MG. Robust Bayesian Inference in Stochastic Frontier Models. Journal of Risk and Financial Management. 2019 Dec 4;183. doi: 10.3390/jrfm12040183

Author

Tsionas, Mike G. / Robust Bayesian Inference in Stochastic Frontier Models. In: Journal of Risk and Financial Management. 2019.

Bibtex

@article{fda239bfc58a41e8a20364c8f78382fe,
title = "Robust Bayesian Inference in Stochastic Frontier Models",
abstract = "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",
keywords = "productivity and efficiency, bayesian analysis, robustness, stochastic frontier models",
author = "Tsionas, {Mike G.}",
year = "2019",
month = dec,
day = "4",
doi = "10.3390/jrfm12040183",
language = "English",
journal = "Journal of Risk and Financial Management",
issn = "1911-8074",
publisher = "MDPI - Open Access Publishing",

}

RIS

TY - JOUR

T1 - Robust Bayesian Inference in Stochastic Frontier Models

AU - Tsionas, Mike G.

PY - 2019/12/4

Y1 - 2019/12/4

N2 - 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

AB - 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

KW - productivity and efficiency

KW - bayesian analysis

KW - robustness

KW - stochastic frontier models

U2 - 10.3390/jrfm12040183

DO - 10.3390/jrfm12040183

M3 - Journal article

JO - Journal of Risk and Financial Management

JF - Journal of Risk and Financial Management

SN - 1911-8074

M1 - 183

ER -