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Some Comments on Copula-Based Regression

Author

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  • Holger Dette
  • Ria Van Hecke
  • Stanislav Volgushev

Abstract

In a recent article, Noh, El Ghouch, and Bouezmarni proposed a new semiparametric estimate of a regression function with a multivariate predictor, which is based on a specification of the dependence structure between the predictor and the response by means of a parametric copula. This comment investigates the effect which occurs under misspecification of the parametric model. We demonstrate by means of several examples that even for a one or two-dimensional predictor the error caused by a "wrong" specification of the parametric family is rather severe, if the regression is not monotone in one of the components of the predictor. Moreover, we also show that these problems occur for all of the commonly used copula families and we illustrate in several examples that the copula-based regression may lead to invalid results even when flexible copula models such as vine copulas (with the common parametric families) are used in the estimation procedure.

Suggested Citation

  • Holger Dette & Ria Van Hecke & Stanislav Volgushev, 2014. "Some Comments on Copula-Based Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1319-1324, September.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:507:p:1319-1324
    DOI: 10.1080/01621459.2014.916577
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    Citations

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    Cited by:

    1. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    2. Paul R. Dewick & Shuangzhe Liu & Yonghui Liu & Tiefeng Ma, 2023. "Elliptical and Skew-Elliptical Regression Models and Their Applications to Financial Data Analytics," JRFM, MDPI, vol. 16(7), pages 1-20, June.
    3. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    4. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    5. Nasri, Bouchra R. & Rémillard, Bruno N. & Bouezmarni, Taoufik, 2019. "Semi-parametric copula-based models under non-stationarity," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 347-365.
    6. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    7. Maximilian Coblenz & Simon Holz & Hans‐Jörg Bauer & Oliver Grothe & Rainer Koch, 2020. "Modelling fuel injector spray characteristics in jet engines by using vine copulas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 863-886, August.
    8. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    9. Nasri, Bouchra R., 2020. "On non-central squared copulas," Statistics & Probability Letters, Elsevier, vol. 161(C).
    10. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.

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