A consistent characteristic function-based test for conditional independence

Y is conditionally independent of Z given X if Pr { f ( y | X , Z ) = f ( y | X ) } = 1 for all y on its support, where f ( · | · ) denotes the conditional density of Y given ( X , Z ) or X . This paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional independence. Discussion Paper, Department of Economics, UCSD) in two directions: (1) our test is less sensitive to the choice of bandwidth sequences; (2) our test has power against deviations on the full support of the density of ( X , Y , Z ). We establish asymptotic normality for our test statistic under weak data dependence conditions. Simulation results suggest that the test is well behaved in finite samples. Applications to stock market data indicate that our test can reveal some interesting nonlinear dependence that a traditional linear Granger causality test fails to detect.