T. Bouezmarni
Nonparametric tests for conditional independence using conditional distributions
Bouezmarni, T.; Taamouti, A.
Abstract
The concept of causality is naturally defined in terms of conditional distribution, however almost all the empirical works focus on causality in mean. This paper aims to propose a nonparametric statistic to test the conditional independence and Granger non-causality between two variables conditionally on another one. The test statistic is based on the comparison of conditional distribution functions using an L2 metric. We use Nadaraya–Watson method to estimate the conditional distribution functions. We establish the asymptotic size and power properties of the test statistic and we motivate the validity of the local bootstrap. We ran a simulation experiment to investigate the finite sample properties of the test and we illustrate its practical relevance by examining the Granger non-causality between S&P 500 Index returns and VIX volatility index. Contrary to the conventional t-test which is based on a linear mean-regression, we find that VIX index predicts excess returns both at short and long horizons.
Citation
Bouezmarni, T., & Taamouti, A. (2014). Nonparametric tests for conditional independence using conditional distributions. Journal of Nonparametric Statistics, 26(4), 697-719. https://doi.org/10.1080/10485252.2014.945447
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 8, 2014 |
Online Publication Date | Aug 15, 2014 |
Publication Date | Oct 2, 2014 |
Deposit Date | Oct 16, 2014 |
Publicly Available Date | Mar 3, 2015 |
Journal | Journal of Nonparametric Statistics |
Print ISSN | 1048-5252 |
Electronic ISSN | 1029-0311 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 26 |
Issue | 4 |
Pages | 697-719 |
DOI | https://doi.org/10.1080/10485252.2014.945447 |
Keywords | Nonparametric tests, Time series, Conditional independence, Granger non-causality, Nadaraya–Watson estimator, Conditional distribution function, VIX volatility index, S&P500 Index. |
Public URL | https://durham-repository.worktribe.com/output/1421834 |
Files
Accepted Journal Article
(264 Kb)
PDF
Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Nonparametric Statistics on 15/08/2014, available online at: http://www.tandfonline.com/10.1080/10485252.2014.945447.
You might also like
Covid-19 Control and the Economy: Test, Test, Test
(2021)
Journal Article
Measuring Granger Causality in Quantiles
(2020)
Journal Article
A better understanding of Granger causality analysis: A big data environment
(2018)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search