Karl Pearson developed the correlation coefficient r(X,Y) in 1890s vastly underestimates dependence between two series. Vinod(2014} develops new generalized correlation coefficients so that when r*(Y|X) r*(X|Y) then X is the "kernel cause'' of Y. Vinod (2015) reports simulations favoring kernel...

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...

Karl Pearson developed the correlation coefficient r(X,Y) in 1890's. Vinod (2014) develops new generalized correlation coefficients so that when r*(Y|X) r*(X|Y) then X is the "kernel cause" of Y. Vinod (2015a) argues that kernel causality amounts to model selection between two kernel...

The R package for maximum entropy bootstrap (meboot) is widely used for numerous applications involving statistical inference for time series data without having to do differencing or de-trending. We report some simulations confirming its effectiveness. It has been used for simulating time...

A popular F test of Granger-causality relies on normally distributed errors of ordinary least squares (OLS) linear regressions. There is a long-standing need for a user-friendly algorithm replacing the OLS by kernel regressions, and the F test by a bootstrap. This paper introduces a version...