School Seminars and Colloquia

A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality

Statistics Seminar

by Jeroen Rombouts


Institution: HEC Montreal
Date: Tue 23rd March 2010
Time: 1:15 PM
Location: Old Geology Theatre 1, The University of Melbourne

Abstract: We discuss a new nonparametric test for conditional independence that
can directly be applied to test for Granger causality. Based on the comparison of copula densities, 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 time series 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 (income, prices and money).

For More Information: contact: Owen Jones. email odjones@unimelb.edu.au