Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark

46 Pages Posted: 8 Mar 2015

See all articles by Hrishikesh D. Vinod

Hrishikesh D. Vinod

Fordham University - Department of Economics

Date Written: March 6, 2015

Abstract

Usual correlations assume linearity. If new generalized correlations satisfy r*(Y |X) > r*(X|Y ), X better predicts Y than vice versa. Then we say that X "causes" Y . Thus, Vinod (2013) revives Granger's instantaneous causality concept. Mooij et al. (2014) and their references seem unaware of causality in econometrics. We propose and illustrate new generalized correlations using benchmark data set Cause Effect Pairs (CEP) that consists of 88 different binary "cause effect pairs" from 31 real world data sources, where the cause is presumed known. Our ability to successfully identify the cause in some 75% of cases means researchers can benefit from using our fairly simple data- analytic R software tools as a first step to save time and expense.

Keywords: Generalized measure of correlation, non-parametric regression, R software

JEL Classification: C10, C20, A20

Suggested Citation

Vinod, Hrishikesh D., Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark (March 6, 2015). Fordham University Schools of Business Research Paper, Available at SSRN: https://ssrn.com/abstract=2574891 or http://dx.doi.org/10.2139/ssrn.2574891

Hrishikesh D. Vinod (Contact Author)

Fordham University - Department of Economics ( email )

Dealy Hall
Bronx, NY 10458
United States
718-817-4065 (Phone)
718-817-3518 (Fax)

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