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Nonlinear Granger Causality: Guidelines for Multivariate Analysis

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  • Diks, C.G.H.

    (University of Amsterdam)

  • Wolski, M.

    (University of Amsterdam)

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.

Suggested Citation

  • Diks, C.G.H. & Wolski, M., 2013. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," CeNDEF Working Papers 13-15, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Handle: RePEc:ams:ndfwpp:13-15
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