期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2009
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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.
