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A Kernel Test of Nonlinear Granger Causality
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.
@inproceedings{5252, title = {A Kernel Test of Nonlinear Granger Causality}, journal = {Proceedings of the Workshop on Inference and Estimation in Probabilistic Time-Series Models}, 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.}, pages = {79-89}, editors = {Barber, D. , A. T. Cemgil, S. Chiappa}, publisher = {Isaac Newton Institute for Mathematical Sciences}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, United Kingdom}, month = jun, year = {2008}, slug = {5252}, author = {Sun, X.}, month_numeric = {6} }