Publication Type
Journal Article
Version
submittedVersion
Publication Date
8-2008
Abstract
We propose a nonparametric test of conditional independence based on the weighted Hellinger distance between the two conditional densities, f(y|x,z) and f(y|x), which is identically zero under the null. We use the functional delta method to expand the test statistic around the population value and establish asymptotic normality under β-mixing conditions. We show that the test is consistent and has power against alternatives at distance n−1/2h−d/4. The cases for which not all random variables of interest are continuously valued or observable are also discussed. Monte Carlo simulation results indicate that the test behaves reasonably well in finite samples and significantly outperforms some earlier tests for a variety of data generating processes. We apply our procedure to test for Granger noncausality in exchange rates.
Keywords
β -mixing, Conditional independence, Functional de lta method, Granger non-causality, Hellinger distance, Local bootstrap, Sample selection bias, U -statistics
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
24
Issue
4
First Page
829
Last Page
864
ISSN
0266-4666
Identifier
10.1017/S0266466608080341
Publisher
Cambridge University Press
Citation
SU, Liangjun and WHITE, Halbert.
A Nonparametric Hellinger Metric Test for Conditional Independence. (2008). Econometric Theory. 24, (4), 829-864.
Available at: https://ink.library.smu.edu.sg/soe_research/248
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S0266466608080341