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A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion

Autor: Sessi Tokpavi, Bertrand Candelon

Publikováno v: Journal of Business and Economic Statistics
Journal of Business and Economic Statistics, Taylor & Francis, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩
Journal of Business and Economic Statistics, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩
Journal of Business & Economic Statistics, 34(2), 240-253. Taylor and Francis

This article introduces a kernel-based nonparametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event. The main advantage of this test

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6464a52b1011483f9e84b6233313d878
https://doi.org/10.1080/07350015.2015.1026774

A Nonparametric Test for Granger-causality in Distribution with Application to Financial Contagion

Autor: Bertrand Caudelon, Sessi Tokpavi

This paper introduces a kernel-based nonparametric inferential procedure to test for Granger-causality in distribution. This test is a multivariate extension of the kernel-based Granger-causality test in tail-event introduced by Hong et al. (2009) an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______645::c03dc35294a0d5b68c4b930ca58e3799
http://economix.fr/pdf/dt/2014/WP_EcoX_2014-18.pdf

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