Generalized Correlation and Kernel Causality with Applications in Development Economics

Hrishikesh D. Vinod

Fordham University - Department of Economics

November 24, 2013

New generalized correlation measures, GMC(Y|X), of 2012 use Kernel regressions to overcome the linearity of Pearson correlation coefficients. A new matrix of generalized correlation coefficients is such that when |r*(i,j)|>|r*(j,i)|, it is more likely that the column variable Xj is what Granger called the "instantaneous cause" or what we call "kernel cause" of the row variable Xi. New partial correlations ameliorate confounding. Various examples and simulations support robustness of new causality. We include bootstrap inference, robustness checks based on dependence between regressor and error and out-of-sample forecasts. Data for 198 countries on nine development variables support growth policy over redistribution and Deaton's criticism of foreign aid. Potential applications include Big Data, since my R code is available in the supplementary material.

Number of Pages in PDF File: 36

Keywords: bootstrap, poverty reduction, foreign aid, entrepreneurship

JEL Classification: O57, O10, C15, C31

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Date posted: November 7, 2013 ; Last revised: January 4, 2015

Suggested Citation

Vinod, Hrishikesh D., Generalized Correlation and Kernel Causality with Applications in Development Economics (November 24, 2013). Available at SSRN: http://ssrn.com/abstract=2350592 or http://dx.doi.org/10.2139/ssrn.2350592

Contact Information

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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