Generalized Correlation and Kernel Causality with Applications in Development Economics

36 Pages Posted: 7 Nov 2013 Last revised: 4 Jan 2015

Hrishikesh D. Vinod

Fordham University - Department of Economics

Date Written: November 24, 2013

Abstract

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.

Keywords: bootstrap, poverty reduction, foreign aid, entrepreneurship

JEL Classification: O57, O10, C15, C31

Suggested Citation

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

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)

Paper statistics

Downloads
96
Rank
226,324
Abstract Views
445