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A Dependence Metric for Possibly Nonlinear Processes

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  • C. W. Granger
  • E. Maasoumi
  • J. Racine

Abstract

A transformed metric entropy measure of dependence is studied which satisfies many desirable properties, including being a proper measure of distance. It is capable of good performance in identifying dependence even in possibly nonlinear time series, and is applicable for both continuous and discrete variables. A nonparametric kernel density implementation is considered here for many stylized models including linear and nonlinear MA, AR, GARCH, integrated series and chaotic dynamics. A related permutation test of independence is proposed and compared with several alternatives. Copyright 2004 Blackwell Publishing Ltd.

Suggested Citation

  • C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, September.
  • Handle: RePEc:bla:jtsera:v:25:y:2004:i:5:p:649-669
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