Nonparametric entropy-based tests of independence between stochastic processes

This paper develops nonparametric tests of independence between two stationary stochastic processes. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, I take advantage of a generalized entropic measure so as to build a class of nonparametric tests of independence. Asymptotic normality and local power are derived using the functional delta method for kernels, whereas finite sample properties are investigated through Monte Carlo simulations.

To cointegrate or not to cointegrate? That's a topological question

We show that for any multivariate I( 1) process which does not cointegrate, it is possible to find another process sufficient1y elose to it where cointegration applies. Closeness is defined in terms of the spectral density matrices of the respective processes in differences, i.e., a metric which takes into account only the information in the (centred) second moments. The result may explain why in practice cointegration is found a bit "too often". Examples developing this point and simulations giving an insight on the metric used are also presented.