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Nonlinear Granger Causality: Guidelines for Multivariate Analysis

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  • Cees Diks
  • Marcin Wolski

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.
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Suggested Citation

  • Cees Diks & Marcin Wolski, 2016. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1333-1351, November.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:7:p:1333-1351
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