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A Dependence Metric for Nonlinear Time Series

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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.

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File URL: http://fmwww.bc.edu/RePEc/es2000/0421a.pdf
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File URL: http://fmwww.bc.edu/RePEc/es2000/0421b.pdf
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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0421.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:0421

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  1. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-78, July.
  2. Robinson, P M, 1991. "Consistent Nonparametric Entropy-Based Testing," Review of Economic Studies, Wiley Blackwell, vol. 58(3), pages 437-53, May.
  3. 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 209-27, February.
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