Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2007

Abstract

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.

Keywords

Conditional characteristic function, Conditional independence, Granger noncausality, Nonparametric regression, U-statistics

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

141

Issue

2

First Page

807

Last Page

834

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2006.11.006

Publisher

Elsevier: 24 months

Additional URL

https://doi.org/10.1016/j.jeconom.2006.11.006

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