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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, 271, 3, 2018 DOI: 10.1016/j.ejor.2018.05.053

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Smooth Approximations to Monotone Concave Functions in Production Analysis: An Alternative to Nonparametric Concave Least Squares

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Published
<mark>Journal publication date</mark>16/12/2018
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Volume271
Number of pages11
Pages (from-to)797-807
Publication StatusPublished
Early online date23/06/18
<mark>Original language</mark>English

Abstract

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.

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, 271, 3, 2018 DOI: 10.1016/j.ejor.2018.05.053