A Kernel Test of Nonlinear Granger Causality

Sun, X

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A Kernel Test of Nonlinear Granger Causality

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Sun, X
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://www-old.newton.ac.uk/programmes/SCH/schw05-papers.pdf
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Citation

Sun, X. (2008). A Kernel Test of Nonlinear Granger Causality. In D. Barber, A. Cemgil, & S. Chiappa (Eds.), Workshop on Inference and Estimation in Probabilistic Time-Series Models (pp. 79-89). Cambridge, United Kingdom: Isaac Newton Institute for Mathematical Sciences.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C8E9-8

Abstract

We present a novel test of nonlinear Granger causality in bivariate time series. The trace norm of conditional covariance operators is used to capture the prediction errors. Based on this measure, a subsampling-based multiple testing procedure tests the prediction improvement of one time series by the other one. The distributional properties of the resulting p-values reveal the direction of Granger causality. Encouraging results of experiments with simulated and real-world data support our approach.