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A Simple Nonparametric Test for Independence

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  • Bruce Mizrach

    ()
    (Rutgers University)

Abstract

A stationary stochastic process is defined to be locally independent if it eventually becomes independent of pastrealizations. I develop a simple nonparametric test for this condition. Size and power comparisons favor this statistic over the one proposed by Brock, Dechert and Scheinkman (1987) in samples under 250 observations.

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Bibliographic Info

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 199523.

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Date of creation: 15 Jan 1995
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Handle: RePEc:rut:rutres:199523

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Keywords: nonlinear dependence; U-statistics;

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Cited by:
  1. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2012. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," AMSE Working Papers 1214, Aix-Marseille School of Economics, Marseille, France.
  2. Mohamed Chikhi & Claude Diebolt, 2006. "Nonparametric Analysis of Financial Time Series by the Kernel Methodology," Working Papers 06-11, Association Française de Cliométrie (AFC).
  3. Fernandes, Marcelo, 2001. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Economics Working Papers (Ensaios Economicos da EPGE) 413, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  4. Peat, Maurice & Stevenson, Max, 1996. "Asymmetry in the business cycle: Evidence from the Australian labour market," Journal of Economic Behavior & Organization, Elsevier, vol. 30(3), pages 353-368, September.
  5. Ishanu Chattopadhyay, 2014. "Causality Networks," Papers 1406.6651, arXiv.org.

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