A Copula Nonlinear Granger Causality

Jong Min Kim; Namgil Lee; Sun Young Hwang

doi:10.1016/j.econmod.2019.09.052

A Copula Nonlinear Granger Causality

Jong Min Kim, Namgil Lee, Sun Young Hwang

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Abstract

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 language	English (US)
Pages (from-to)	420-430
Number of pages	11
Journal	Economic Modelling
Volume	88
DOIs	https://doi.org/10.1016/j.econmod.2019.09.052
State	Published - 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.

Keywords

Copula
Directional dependence
Granger causality
Permutation test

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title = "A Copula Nonlinear Granger Causality",

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AU - Lee, Namgil

AU - Hwang, Sun Young

N1 - 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.

PY - 2020/6

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N2 - 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.

AB - 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.

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A Copula Nonlinear Granger Causality

Abstract

Bibliographical note

Keywords

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