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Econometrics 2017, 5(3), 33; doi:10.3390/econometrics5030033

Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity

1
Enterprise Risk Solutions, Moody’s Analytics Inc., San Francisco, CA 94105, USA
2
Department of Economics, Rice University, Houston, TX 77005, USA
3
Department of Economics, Lancaster University Management School, Lancaster LA14YX, UK
*
Author to whom correspondence should be addressed.
Academic Editors: In Choi and Ryo Okui
Received: 22 May 2017 / Revised: 12 June 2017 / Accepted: 21 June 2017 / Published: 28 July 2017
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
View Full-Text   |   Download PDF [585 KB, uploaded 28 July 2017]   |  

Abstract

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. View Full-Text
Keywords: panel data; time-varying heterogeneity; Bayesian econometrics; banking studies; productivity panel data; time-varying heterogeneity; Bayesian econometrics; banking studies; productivity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, J.; Sickles, R.C.; Tsionas, E.G. Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity. Econometrics 2017, 5, 33.

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