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Partial Symbolic Transfer Entropy

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Author Info

  • Diks, C.G.H.

    ()
    (University of Amsterdam)

  • Papana, A.
  • Kyrtsou, K.
  • Kugiumtzis, D.
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    Abstract

    In this paper, we introduce the partial symbolic transfer entropy (PSTE), an extension of the symbolic transfer entropy that accounts only for the direct causal effects among the components of a multivariate system. It is an information theoretic measure, and as such does not suffer from model mis-specification bias. The PSTE is defined on the ranks of vectors that are formed from the reconstructed vectors, instead of the original time series values. The statistical significance of PSTE is assessed by randomization test making use of surrogate time series. The PSTE is evaluated on multivariate time series of different types of coupled and uncoupled systems and compared with conditional Granger causality index (CGCI). It is shown that the PSTE is not affected by the existence of outliers, it is directly applicable to time series that are non-stationary in mean and in variance, and it is also not affected by data filtering. As a real application, the causal effects among three economic indexes are investigated. Computations of PSTE and CGCI on both the initial returns and the VAR filtered returns, and only of PSTE on the original indexes, showed consistency of the PSTE in estimating the causal effect.

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    File URL: http://www1.fee.uva.nl/cendef/publications/papers/PSTE_working_paper.pdf
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    Bibliographic Info

    Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 13-16.

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    Date of creation: 2013
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    Handle: RePEc:ams:ndfwpp:13-16

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    Postal: Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands
    Phone: + 31 20 525 52 58
    Fax: + 31 20 525 52 83
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    Web page: http://www.fee.uva.nl/cendef/
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    1. Ke-Li Xu & Peter C.B. Phillips, 2006. "Adaptive Estimation of Autoregressive Models with Time-Varying Variances," Cowles Foundation Discussion Papers 1585R, Cowles Foundation for Research in Economics, Yale University, revised Nov 2006.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
    3. Hiemstra, Craig & Jones, Jonathan D, 1994. " Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-64, December.
    4. Kyrtsou, Catherine & Malliaris, Anastasios G., 2009. "The impact of information signals on market prices when agents have non-linear trading rules," Economic Modelling, Elsevier, vol. 26(1), pages 167-176, January.
    5. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
    6. Jen-Chi Cheng & Larry Taylor & Wenlong Weng, 2010. "The links between international parity conditions and Granger causality: a study of exchange rates and prices," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3491-3501.
    7. Baghli, Mustapha, 2006. "A model-free characterization of causality," Economics Letters, Elsevier, vol. 91(3), pages 380-388, June.
    8. Karagianni Stella & Kyrtsou Catherine, 2011. "Analysing the Dynamics between U.S. Inflation and Dow Jones Index Using Non-Linear Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-25, March.
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