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Testing Conditional Independence Via Empirical Likelihood

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Author Info

  • Su, Liangjun
  • White, Halbert

Abstract

Let f(y|x,z) (resp. f(y|x) be the conditional density of Y given (X,Z) (resp. X). We construct a class of `smoothed` empirical likelihood-based tests for the conditional independence hypothesis: Pr[f(Y|X,Z)=f(Y|X)]=1. We show that the test statistics are asymptotically normal under the null hypothesis and derive their asymptotic distributions under a sequence of local alternatives. The tests are shown to possess a weak optimality property in large samples. Simulation results suggest that the tests behave well in finite samples. Applications to some economic and financial time series indicate that our tests reveal some interesting nonlinear causal relations which the traditional linear Granger causality test fails to detect.

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Bibliographic Info

Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt35v8g0fm.

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Date of creation: 01 Oct 2003
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Handle: RePEc:cdl:ucsdec:qt35v8g0fm

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Related research

Keywords: Conditional Independence; b-mixing;

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Cited by:
  1. Taoufik Bouezmarni & Jeroen Rombouts & Abderrahim Taamouti, 2009. "A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality," CIRANO Working Papers 2009s-28, CIRANO.
  2. de Luna, Xavier & Waernbaum, Ingeborg, 2005. "Covariate selection for non-parametric estimation of treatment effects," Working Paper Series 2005:4, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  3. Yoon-Jae Whang, 2003. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Econometrics 0310005, EconWPA.
  4. Kyungchul Song, 2007. "Testing Conditional Independence via Rosenblatt Transforms," PIER Working Paper Archive 07-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

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