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Nonparametric estimation and inference for conditional density based Granger causality measures.

Taamouti, A. and Bouezmarni, T. and El Gouch, A. (2014) 'Nonparametric estimation and inference for conditional density based Granger causality measures.', Journal of econometrics., 180 (2). pp. 251-264.

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.

Item Type:Article
Keywords:Causality measures, Nonparametric estimation, Time series, Bernstein copula density, Local bootstrap, Exchange rates, Volatility index, Dividend–price ratio, Liquidity stock returns.
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1016/j.jeconom.2014.03.001
Publisher 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.
Record Created:10 Nov 2014 09:35
Last Modified:03 Mar 2015 12:58

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