Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes

Marcelo Fernandes and Breno Neri ()

Econometric Reviews, 2010, vol. 29, issue 3, 276-306

Abstract: This article develops nonparametric tests of independence between two stochastic processes satisfying β-mixing conditions. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, we take advantage of a generalized entropic measure so as to build a whole family of nonparametric tests of independence. We derive asymptotic normality and local power using the functional delta method for kernels. As a corollary, we also develop a class of entropy-based tests for serial independence. The latter are nuisance parameter free, and hence also qualify for dynamic misspecification analyses. We then investigate the finite-sample properties of our serial independence tests through Monte Carlo simulations. They perform quite well, entailing more power against some nonlinear AR alternatives than two popular nonparametric serial-independence tests.

Keywords: Independence; Misspecification testing; Nonparametric theory; Tsallis entropy (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (7)

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Working Paper: Nonparametric entropy-based tests of independence between stochastic processes (2001)
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DOI: 10.1080/07474930903451557

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