A dependence metric for possibly nonlinear processes

被引：127

作者：

Granger, CW

Maasoumi, E ^{[1
]}

Racine, J

机构：

[1] So Methodist Univ, Dept Econ, Dallas, TX 75275 USA

[2] Univ Calif San Diego, San Diego, CA 92103 USA

[3] Syracuse Univ, Syracuse, NY 13244 USA

来源：

JOURNAL OF TIME SERIES ANALYSIS | 2004年 / 25卷 / 05期

关键词：

entropy; information theory; nonlinear models; serial dependence; nonparametric; goodness of fit; bootstrap;

D O I：

10.1111/j.1467-9892.2004.01866.x

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：649 / 669

页数：21

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