“Generalized measures of correlation for asymmetry, nonlinearity, and beyond”: Some antecedents on causality

Authors

David E. Allen, Edith Cowan UniversityFollow
Michael McAleer

Document Type

Journal Article

Publication Title

Journal of the American Statistical Association

ISSN

01621459

Publisher

Taylor and Francis

School

School of Business and Law

RAS ID

35206

Funders

Australian Research Council / Ministry of Science and Technology (MOST), Taiwan

Comments

Allen, D. E., & McAleer, M. (2022). “Generalized measures of correlation for asymmetry, nonlinearity, and beyond”: Some antecedents on causality. Journal of the American Statistical Association, 117(537), 214-224. https://doi.org/10.1080/01621459.2020.1768101

Abstract

© 2020, © 2020 American Statistical Association. This note comments on the generalized measure of correlation (GMC) that was suggested by Zheng, Shi, and Zhang. The GMC concept was partly anticipated in some publications over 100 years earlier by Yule in the Proceedings of the Royal Society, and by Kendall. Other antecedents discussed include work on dependency by Renyi and Doksum and Samarov, together with the Yule–Simpson paradox. The GMC metric partly extends the concept of Granger causality, so that we consider causality, graphical analysis and alternative measures of dependency provided by copulas.

DOI

10.1080/01621459.2020.1768101

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