Open AccessThis article is
- freely available
Simulation Study of Direct Causality Measures in Multivariate Time Series
Department of Economics, University of Macedonia, Egnatias 156, 54006, Thessaloniki, Greece
University of Strasbourg, BETA, University of Paris 10, Economix, ISC-Paris, Ile-de-France, France
Faculty of Engineering, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece
Faculty of Economics, Department of Economics and Econometrics, University of Amsterdam, Valckenierstraat 65-67, 1018 XE, Amsterdam, The Netherlands
* Author to whom correspondence should be addressed.
Received: 28 March 2013; in revised form: 5 June 2013 / Accepted: 27 June 2013 / Published: 4 July 2013
Abstract: Measures of the direction and strength of the interdependence among time series from multivariate systems are evaluated based on their statistical significance and discrimination ability. The best-known measures estimating direct causal effects, both linear and nonlinear, are considered, i.e., conditional Granger causality index (CGCI), partial Granger causality index (PGCI), partial directed coherence (PDC), partial transfer entropy (PTE), partial symbolic transfer entropy (PSTE) and partial mutual information on mixed embedding (PMIME). The performance of the multivariate coupling measures is assessed on stochastic and chaotic simulated uncoupled and coupled dynamical systems for different settings of embedding dimension and time series length. The CGCI, PGCI and PDC seem to outperform the other causality measures in the case of the linearly coupled systems, while the PGCI is the most effective one when latent and exogenous variables are present. The PMIME outweighs all others in the case of nonlinear simulation systems.
Keywords: direct Granger causality; multivariate time series; information measures
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Papana, A.; Kyrtsou, C.; Kugiumtzis, D.; Diks, C. Simulation Study of Direct Causality Measures in Multivariate Time Series. Entropy 2013, 15, 2635-2661.
Papana A, Kyrtsou C, Kugiumtzis D, Diks C. Simulation Study of Direct Causality Measures in Multivariate Time Series. Entropy. 2013; 15(7):2635-2661.
Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees. 2013. "Simulation Study of Direct Causality Measures in Multivariate Time Series." Entropy 15, no. 7: 2635-2661.