Nonparametric estimation and inference for conditional density based Granger causality measures

Abderrahim Taamouti, Taoufik Bouezmarni and Anouar El Ghouch

Journal of Econometrics, 2014, vol. 180, issue 2, 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.

Keywords: Causality measures; Nonparametric estimation; Time series; Bernstein copula density; Local bootstrap; Exchange rates; Volatility index; Dividend–price ratio; Liquidity stock returns (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 C19 E3 E4 G1 G12 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Working Paper: Nonparametric estimation and inference for conditional density based Granger causality measures (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:180:y:2014:i:2:p:251-264

DOI: 10.1016/j.jeconom.2014.03.001

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