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.
- Sprache
-
Englisch
- Erschienen in
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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
- Klassifikation
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Wirtschaft
- Thema
-
bayesian analysis
productivity and efficiency
robustness
stochastic frontier models
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Tsionas, Efthymios G.
- Ereignis
-
Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
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doi:10.3390/jrfm12040183
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Tsionas, Efthymios G.
- MDPI
Entstanden
- 2019
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