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Choosing the optimal model parameters for Granger causality in application to time series with main timescale

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  • Kornilov, Maksim V.
  • Medvedeva, Tatiana M.
  • Bezruchko, Boris P.
  • Sysoev, Ilya V.

Abstract

The problem of determining the presence and direction of coupling between experimentally observed time series is of immediate interest in many relevant areas of knowledge. One of the approaches to its solution is the method of nonlinear Granger causality. The algorithm is based on the construction of predictive models and its effectiveness depends on the proper selection of model parameters.

Suggested Citation

  • Kornilov, Maksim V. & Medvedeva, Tatiana M. & Bezruchko, Boris P. & Sysoev, Ilya V., 2016. "Choosing the optimal model parameters for Granger causality in application to time series with main timescale," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 11-21.
    • Handle: RePEc:eee:chsofr:v:82:y:2016:i:c:p:11-21
    DOI: 10.1016/j.chaos.2015.10.027
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    References listed on IDEAS

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    1. Luković, Mirko & Vanni, Fabio & Svenkeson, Adam & Grigolini, Paolo, 2014. "Transmission of information at criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 430-438.
    2. Vasily A Vakorin & Bratislav Mišić & Olga Krakovska & Gleb Bezgin & Anthony R McIntosh, 2013. "Confounding Effects of Phase Delays on Causality Estimation," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Wang, Mingzhao & Fu, Zuntao, 2022. "A new method of nonlinear causality detection: Reservoir computing Granger causality," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).

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