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, 271, 3, 2018 DOI: 10.1016/j.ejor.2018.05.053
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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
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TY - JOUR
T1 - Smooth Approximations to Monotone Concave Functions in Production Analysis
T2 - An Alternative to Nonparametric Concave Least Squares
AU - Tsionas, Efthymios
AU - Izzeldin, Marwan
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, 271, 3, 2018 DOI: 10.1016/j.ejor.2018.05.053
PY - 2018/12/16
Y1 - 2018/12/16
N2 - Estimation of banking efficiency and productivity is essential for regulatory purposes and for testing various theories in the context of banking such as the quiet life hypothesis, the bad management hypothesis etc. In such studies it is, therefore, important to place as few restrictions as possible on the functional forms subject to global satisfaction of the theoretical properties relating to monotonicity and concavity. In this paper we propose an alternative to nonparametric segmented concave least squares. We use a differentiable approximation to an arbitrary functional form based on smoothly mixing Cobb-Douglas anchor functions over the data space. Estimation is based on Bayesian techniques organized around Markov Chain Monte Carlo. The approximation properties of the new functional form are investigated in a Monte Carlo experiment where the true functional form is a Symmetric Generalized McFadden. The new techniques are applied to a large U.S banking data set as well as a global banking data set.
AB - Estimation of banking efficiency and productivity is essential for regulatory purposes and for testing various theories in the context of banking such as the quiet life hypothesis, the bad management hypothesis etc. In such studies it is, therefore, important to place as few restrictions as possible on the functional forms subject to global satisfaction of the theoretical properties relating to monotonicity and concavity. In this paper we propose an alternative to nonparametric segmented concave least squares. We use a differentiable approximation to an arbitrary functional form based on smoothly mixing Cobb-Douglas anchor functions over the data space. Estimation is based on Bayesian techniques organized around Markov Chain Monte Carlo. The approximation properties of the new functional form are investigated in a Monte Carlo experiment where the true functional form is a Symmetric Generalized McFadden. The new techniques are applied to a large U.S banking data set as well as a global banking data set.
KW - OR in Banking
KW - Simulation
KW - Nonparametric Concave Least Squares
KW - Segmented Least Squares
KW - Bayesian analysis
U2 - 10.1016/j.ejor.2018.05.053
DO - 10.1016/j.ejor.2018.05.053
M3 - Journal article
VL - 271
SP - 797
EP - 807
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -