Publication details for Prof Abderrahim TaamoutiTaamouti, 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.
- Publication type: Journal Article
- ISSN/ISBN: 0304-4076
- DOI: 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.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.