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Nonparametric estimation and inference for conditional density based Granger causality measures

Listed author(s):
  • Taamouti, Abderrahim
  • Bouezmarni, Taoufik
  • El Ghouch, Anouar

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0304407614000402
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 180 (2014)
Issue (Month): 2 ()
Pages: 251-264

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Handle: RePEc:eee:econom:v:180:y:2014:i:2:p:251-264
DOI: 10.1016/j.jeconom.2014.03.001
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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