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    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 282, 3, 2020 DOI: 10.1016/10.1016/j.ejor.2019.10.012

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Quantile Stochastic Frontiers

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Published
<mark>Journal publication date</mark>1/05/2020
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Volume282
Number of pages8
Pages (from-to)1177-1184
Publication StatusPublished
Early online date22/11/19
<mark>Original language</mark>English

Abstract

In this paper, based on Jradi and Ruggiero (2019). Stochastic Data Envelopment Analysis: A Quantile Regression Approach to Estimate the Production Frontier. European Journal of Operational Research, 278 (2), 385–393] we propose a novel quantile Stochastic Frontier Model (SFM) and develop Markov Chain Monte Carlo techniques for numerical Bayesian inference. In an empirical application to US large banks we document important differences between the Quantile and the traditional SFM, in terms of several aspects of the data. We also document considerable heterogeneity among different quantiles in terms of returns to scale, technical change, efficiency change, technical efficiency, as well as productivity growth.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 282, 3, 2020 DOI: 10.1016/10.1016/j.ejor.2019.10.012