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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Quantile Stochastic Frontiers
AU - Tsionas, Mike G.
N1 - 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
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Productivity and competitiveness
KW - Efficiency
KW - Quantile Stochastic Frontier model
KW - Bayesian Inference
U2 - 10.1016/j.ejor.2019.10.012
DO - 10.1016/j.ejor.2019.10.012
M3 - Journal article
VL - 282
SP - 1177
EP - 1184
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -