Journal
JOURNAL OF TIME SERIES ANALYSIS
Volume 25, Issue 5, Pages 649-669Publisher
WILEY
DOI: 10.1111/j.1467-9892.2004.01866.x
Keywords
entropy; information theory; nonlinear models; serial dependence; nonparametric; goodness of fit; bootstrap
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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.
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