Directed information graphs for the Granger causality of multivariate time series

Wei Gao, Wanqi Cui and Wenna Ye

Physica A: Statistical Mechanics and its Applications, 2017, vol. 486, issue C, 701-710

Abstract: In this paper, we investigate the links between (strong) Granger causality and directed information theory for multivariate time series. Based on the decomposition of conditional directed information, we propose a definition of Granger causality including instantaneous variables in the conditional set, which can avoid the spurious causality. The directed information graphs are presented to describe the Granger causality and instantaneous coupling. The structure learning of the graph models is based on the Leonenko’s k-nn estimator of the statistics and a permutation test of the significant. Finally, we demonstrate the numerical implementation of these techniques on linear and nonlinear time series.

Keywords: Directed information; Granger causality; Multivariate time series; Graphical model (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:486:y:2017:i:c:p:701-710

DOI: 10.1016/j.physa.2017.05.035

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