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A Dependence Metric for Nonlinear Time Series

Author

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  • C. W. Granger

    (University of California)

  • Esfandiar Maasoumi

    (Southern Methodist University)

Abstract

A transformed metric entropy measure of dependence is studied which satisfies several desirable properties and is capable of impressive performance in identifying nonlinear dependence in time series. The measure is applicable for both continuous and discrete variables. A nonparametric kernel density implementation is considered here for ten models including MA, AR, integrated series and chaotic dynamics.

Suggested Citation

  • C. W. Granger & Esfandiar Maasoumi, 2000. "A Dependence Metric for Nonlinear Time Series," Econometric Society World Congress 2000 Contributed Papers 0421, Econometric Society.
    • Handle: RePEc:ecm:wc2000:0421
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    References listed on IDEAS

    as
    1. Clive Granger & Jin‐Lung Lin, 1994. "Using The Mutual Information Coefficient To Identify Lags In Nonlinear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(4), pages 371-384, July.
    2. Rilstone, Paul, 1991. "Nonparametric Hypothesis Testing with Parametric Rates of Convergence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(1), pages 209-227, February.
    3. P. M. Robinson, 1991. "Consistent Nonparametric Entropy-Based Testing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 437-453.
    4. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-378, July.
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