On a High-Dimensional Model Representation method based on Copulas

Tsionas, Mike G.; Andrikopoulos, Athanasios

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).

Citation

Tsionas, M. G., & Andrikopoulos, A. (2020). On a High-Dimensional Model Representation method based on Copulas. European journal of operational research, 284(3), 967-979. https://doi.org/10.1016/j.ejor.2020.01.026

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2020
• Online Publication Date: Jan 18, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Feb 12, 2020
• Publicly Available Date: Jan 19, 2022
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 284
• Issue: 3
• Pages: 967-979
• DOI: https://doi.org/10.1016/j.ejor.2020.01.026
• Keywords: Productivity and Competitiveness; Copula; High Dimensional Model Representation; Multilayer perceptron; Bayesian analysis
• Public URL: https://hull-repository.worktribe.com/output/3428072
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0377221720300473?via%3Dihub