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Generalized information matrix tests for copulas

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  • Artem Prokhorov
  • Ulf Schepsmeier
  • Yajing Zhu

Abstract

We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White and so relate to the copula test proposed by Huang and Prokhorov. The idea is that eigenspectrum-based statements of the IM equality reduce the degrees of freedom of the test’s asymptotic distribution and lead to better size-power properties, even in high dimensions. The gains are especially pronounced for vine copulas, where additional benefits come from simplifications of score functions and the Hessian. We derive the asymptotic distribution of the generalized tests, accounting for the nonparametric estimation of the marginals and apply a parametric bootstrap procedure, valid when asymptotic critical values are inaccurate. In Monte Carlo simulations, we study the behavior of the new tests, compare them with several Cramer–von Mises type tests and confirm the desired properties of the new tests in high dimensions.

Suggested Citation

  • Artem Prokhorov & Ulf Schepsmeier & Yajing Zhu, 2019. "Generalized information matrix tests for copulas," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1024-1054, October.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:9:p:1024-1054
    DOI: 10.1080/07474938.2018.1514023
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    Cited by:

    1. Tao Sun & Yu Cheng & Ying Ding, 2023. "An information ratio‐based goodness‐of‐fit test for copula models on censored data," Biometrics, The International Biometric Society, vol. 79(3), pages 1713-1725, September.
    2. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2016. "Generalized Information Matrix Tests for Detecting Model Misspecification," Econometrics, MDPI, vol. 4(4), pages 1-24, November.
    3. Santiago Pereda-Fernández, 2021. "Copula-Based Random Effects Models for Clustered Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 575-588, March.
    4. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.

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