A Copula Nonlinear Granger Causality

Jong Min Kim, Namgil Lee, Sun Young Hwang

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations


    We propose a new copula nonlinear Granger causality test that is more robust than the current available linear and nonlinear Granger causality tests when there exists an asymmetric and nonlinear directional dependence. To perform the statistical test of the copula nonlinear causality, the Gaussian Copula Marginal Regression (GCMR) model and copula directional dependence (Kim and Hwang, 2017) are employed in this paper. By using GCMR and two-sample permutation test with rank sum statistic for the copula nonlinear Granger causality, we can confirm that the result of the proposed copula nonlinear Granger causality test is a reliable test through the simulated data and real data both for small and large sample sizes.

    Original languageEnglish (US)
    Pages (from-to)420-430
    Number of pages11
    JournalEconomic Modelling
    StatePublished - Jun 2020

    Bibliographical note

    Funding Information:
    We thank the two Reviewers and AE for careful reading and constructive comments which led to substantial improvements in the revised version. This work was supported by a grant from the National Research Foundation of Korea ( NRF-2018R1A2B2004157 ).

    Publisher Copyright:
    © 2019 Elsevier B.V.


    • Copula
    • Directional dependence
    • Granger causality
    • Permutation test


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