Nonlinear Granger Causality: Guidelines for Multivariate Analysis

Cees Diks () and Marcin Wolski

No 13-15, CeNDEF Working Papers from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance

Abstract: In this paper we propose an extension of the nonparametric Granger causality test, originally introduced by Diks and Panchenko [2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics \& Control 30, 1647-1669]. We show that the basic test statistics lacks consistency in the multivariate setting. The problem is the result of the kernel density estimator bias, which does not converge to zero at a sufficiently fast rate when the number of conditioning variables is larger than one. In order to overcome this difficulty we apply the data-sharpening method for bias reduction. We then derive the asymptotic properties of the `sharpened' test statistics and we investigate its performance numerically. We conclude with an empirical application to the US grain market.

Date: 2013
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Journal Article: Nonlinear Granger Causality: Guidelines for Multivariate Analysis (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ams:ndfwpp:13-15

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