Artikel
Robust Bayesian inference in stochastic frontier models
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 12 ; Year: 2019 ; Issue: 4 ; Pages: 1-9 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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bayesian analysis
productivity and efficiency
robustness
stochastic frontier models
- Event
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Geistige Schöpfung
- (who)
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Tsionas, Efthymios G.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2019
- DOI
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doi:10.3390/jrfm12040183
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Tsionas, Efthymios G.
- MDPI
Time of origin
- 2019
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