A Dependence Metric for Possibly Nonlinear Processes
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.
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Volume (Year): 25 (2004)
Issue (Month): 5 (09)
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