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A nonparametric copula based test for conditional independence with applications to granger causality

Taoufik Bouezmarni, Jeroen VK Rombouts () and Abderrahim Taamouti ()

Economics Working Papers from Universidad Carlos III, Departamento de Economía

Abstract: This paper proposes a new nonparametric test for conditional independence, which is based on the comparison of Bernstein copula densities using the Hellinger distance. The test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establish local power properties, and motivate the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the good size and power properties of the test. We illustrate the empirical relevance of our test by focusing on Granger causality using financial time series data to test for nonlinear leverage versus volatility feedback effects and to test for causality between stock returns and trading volume. In a third application, we investigate Granger causality between macroeconomic variables

Keywords: Nonparametric tests; Conditional independence; Granger non-causality; Bernstein density copula; Bootstrap; Finance; Volatility asymmetry; Leverage effect; Volatility feedback effect; Macroeconomics (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 C19 G1 G12 E3 E4 E52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2009-06

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Related works:
Working Paper: A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality (2009) Downloads
Working Paper: A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality (2009) Downloads
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