A Dependence Metric for Possibly Nonlinear Processes

Clive W. J. Granger, Esfandiar Maasoumi () and Jeffrey Scott Racine ()

Journal of Time Series Analysis, 2004, vol. 25, issue 5, pages 649-669

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.

Date: 2004
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