A Dependence Metric for Possibly Nonlinear Processes

21 Pages Posted: 7 Sep 2004

See all articles by Clive W. J. Granger

Clive W. J. Granger

University of California, San Diego (UCSD) - Department of Economics; Tinbergen Institute

Esfandiar Maasoumi

Southern Methodist University (SMU) - Department of Economics

Jeffrey Racine

Syracuse University

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.

Suggested Citation

Granger, Clive W. J. and Maasoumi, Esfandiar (Essie) and Racine, Jeffrey, A Dependence Metric for Possibly Nonlinear Processes. Available at SSRN: https://ssrn.com/abstract=573014

Clive W. J. Granger (Contact Author)

University of California, San Diego (UCSD) - Department of Economics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0508
United States
858-534-3383 (Phone)
858-534-7040 (Fax)

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

Esfandiar (Essie) Maasoumi

Southern Methodist University (SMU) - Department of Economics ( email )

Dallas, TX 75275
United States
(214) 768-4298 (Phone)
(214) 768-1821 (Fax)

Jeffrey Racine

Syracuse University ( email )

900 S. Crouse Avenue
Syracuse, NY 13244-2130
United States

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