Quantile Stochastic Frontiers

Tsionas, Mike G. (2020) Quantile Stochastic Frontiers. European Journal of Operational Research, 282 (3). pp. 1177-1184. ISSN 0377-2217

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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.

Item Type:

Journal Article

Journal or Publication Title:

European Journal of Operational Research

Additional Information:

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

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2600/2611

Subjects:

?? productivity and competitivenessefficiencyquantile stochastic frontier modelbayesian inferencemodelling and simulationmanagement science and operations researchinformation systems and management ??

Departments:

Lancaster University Management School > Economics

ID Code:

149584

Deposited By:

ep_importer_pure

Deposited On:

08 Dec 2020 15:48

Refereed?:

Yes

Published?:

Published