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A conditional independence test for dependent data based on maximal conditional correlation

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  • Cheng, Yu-Hsiang
  • Huang, Tzee-Ming

Abstract

In Huang (2010) [8], a test of conditional independence based on maximal nonlinear conditional correlation is proposed and the asymptotic distribution for the test statistic under conditional independence is established for IID data. In this paper, we derive the asymptotic distribution for the test statistic under conditional independence for α-mixing data. The results of simulation show that the test performs reasonably well for dependent data. We also apply the test to stock index data to test Granger noncausality between returns and trading volume.

Suggested Citation

  • Cheng, Yu-Hsiang & Huang, Tzee-Ming, 2012. "A conditional independence test for dependent data based on maximal conditional correlation," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 210-226.
    • Handle: RePEc:eee:jmvana:v:107:y:2012:i:c:p:210-226
    DOI: 10.1016/j.jmva.2012.01.017
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    References listed on IDEAS

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    1. Taoufik Bouezmarni & Jeroen V.K. Rombouts & Abderrahim Taamouti, 2011. "Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 275-287, October.
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