Artikel
Bayesian treatments for panel data stochastic frontier models with time varying heterogeneity
This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of unobserved effects. The latter have been addressed in a non-Bayesian framework by Bai (2009) and Kneip et al. (2012), among others. Monte Carlo experiments are used to examine the finite-sample performance of our estimators. An empirical study of efficiency trends in the largest banks operating in the U.S. from 1990 to 2009 illustrates our new estimators. The study concludes that scale economies in intermediation services have been largely exploited by these large U.S. banks.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 5 ; Year: 2017 ; Issue: 3 ; Pages: 1-21 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Bayesian Analysis: General
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
- Thema
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panel data
time-varying heterogeneity
Bayesian econometrics
banking studies
productivity
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Liu, Junrong
Sickles, Robin C.
Tsionas, E. G.
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2017
- DOI
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doi:10.3390/econometrics5030033
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 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
- Liu, Junrong
- Sickles, Robin C.
- Tsionas, E. G.
- MDPI
Entstanden
- 2017
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