On a High-Dimensional Model Representation method based on Copulas

Tsionas, Mike G. and Andrikopoulos, Athanasios (2020) On a High-Dimensional Model Representation method based on Copulas. European Journal of Operational Research, 284 (3). pp. 967-979. ISSN 0377-2217

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Abstract

This article provides an alternative to High-Dimensional Model Representation (HDMR) using a Copula approximation of an unknown functional form. We apply our methodology in the context of an extensive Monte Carlo study and to a sample of large US commercial banks. In the Monte Carlo experiment, the approximations errors of the Copula approach are small and behave randomly. In our empirical application, we find that the Copula Approximation performs much better, in terms of Bayes factors for model comparison, compared to HDMR, which, in turn, provides better results when compared with standard flexible functional forms, like the translog, the minflex Laurent, and the Generalized Leontief, or a Multilayer Perceptron. Moreover, the choice of approximation has significant implications for productivity and its components (returns to scale, technical inefficiency, technical change, and efficiency change).

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, 284, 3, 2020 DOI: 10.1016/j.ejor.2020.01.026
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code:
149577
Deposited By:
Deposited On:
02 Dec 2020 09:59
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Jan 2021 12:47