Nonparametric estimation and inference for conditional density based Granger causality measures

Taamouti, A.; Bouezmarni, T.; El Gouch, A.

doi:10.1016/j.jeconom.2014.03.001

Nonparametric estimation and inference for conditional density based Granger causality measures

Taamouti, A.; Bouezmarni, T.; El Gouch, A.

Authors

Professor Abderrahim Taamouti abderrahim.taamouti@durham.ac.uk
Visiting Fellow

T. Bouezmarni

A. El Gouch

Abstract

We propose a nonparametric estimation and inference for conditional density based Granger causality measures that quantify linear and nonlinear Granger causalities. We first show how to write the causality measures in terms of copula densities. Thereafter, we suggest consistent estimators for these measures based on a consistent nonparametric estimator of copula densities. Furthermore, we establish the asymptotic normality of these nonparametric estimators and discuss the validity of a local smoothed bootstrap that we use in finite sample settings to compute a bootstrap bias-corrected estimator and to perform statistical tests. A Monte Carlo simulation study reveals that the bootstrap bias-corrected estimator behaves well and the corresponding test has quite good finite sample size and power properties for a variety of typical data generating processes and different sample sizes. Finally, two empirical applications are considered to illustrate the practical relevance of nonparametric causality measures.

Citation

Taamouti, A., Bouezmarni, T., & El Gouch, A. (2014). Nonparametric estimation and inference for conditional density based Granger causality measures. Journal of Econometrics, 180(2), 251-264. https://doi.org/10.1016/j.jeconom.2014.03.001

Journal Article Type	Article
Acceptance Date	Mar 3, 2014
Online Publication Date	Mar 14, 2014
Publication Date	Jun 1, 2014
Deposit Date	Aug 28, 2014
Publicly Available Date	Mar 3, 2015
Journal	Journal of Econometrics
Print ISSN	0304-4076
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	180
Issue	2
Pages	251-264
DOI	https://doi.org/10.1016/j.jeconom.2014.03.001
Keywords	Causality measures, Nonparametric estimation, Time series, Bernstein copula density, Local bootstrap, Exchange rates, Volatility index, Dividend–price ratio, Liquidity stock returns.
Public URL	https://durham-repository.worktribe.com/output/1421923

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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, 180, 2, June 2014, 10.1016/j.jeconom.2014.03.001.