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On a High-Dimensional Model Representation method based on Copulas

Tsionas, Mike G.; Andrikopoulos, Athanasios

Authors

Mike G. Tsionas

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Dr Thanos Andrikopoulos A.Andrikopoulos@hull.ac.uk
Lecturer in Finance & Programme Director BSc Financial Management at University of Hull



Abstract

This article provides an alternative to High-Dimensional Model Representation 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 High-Dimensional Model Representation, 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).

Journal Article Type Article
Journal European Journal of Operational Research
Print ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
APA6 Citation Tsionas, M. G., & Andrikopoulos, A. (in press). On a High-Dimensional Model Representation method based on Copulas. European journal of operational research, https://doi.org/10.1016/j.ejor.2020.01.026
DOI https://doi.org/10.1016/j.ejor.2020.01.026
Keywords Management Science and Operations Research;Productivity and Competitiveness; Copula; High Dimensional Model Representation; Multilayer perceptron; Bayesian analysis

This file is under embargo due to copyright reasons.

Contact A.Andrikopoulos@hull.ac.uk to request a copy for personal use.




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