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Nonparametric estimation and inference for conditional density based Granger causality measures

Author

Listed:
  • Taamouti, Abderrahim
  • Bouezmarni, Taoufik
  • El Ghouch, Anouar

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Taamouti, Abderrahim & Bouezmarni, Taoufik & El Ghouch, Anouar, 2014. "Nonparametric estimation and inference for conditional density based Granger causality measures," LIDAM Reprints ISBA 2014025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2014025
    Note: In : Journal of Econometrics, vol. 180, no.2, p. 251-264 (2014)
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    Cited by:

    1. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2024. "Testing Granger non-causality in expectiles," Econometric Reviews, Taylor & Francis Journals, vol. 43(1), pages 30-51, January.
    2. Mayer, Alexander & Wied, Dominik & Troster, Victor, 2025. "Quantile Granger causality in the presence of instability," Journal of Econometrics, Elsevier, vol. 249(PB).
    3. Ouimet, Frédéric, 2021. "Asymptotic properties of Bernstein estimators on the simplex," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    4. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
    5. Bertrand Candelon & Sessi Tokpavi, 2016. "A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 240-253, April.
    6. Li, Haiqi & Zhong, Wanling & Park, Sung Y., 2016. "Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations," Economic Modelling, Elsevier, vol. 52(PB), pages 661-671.
    7. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    8. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.
    9. Huang, Jing & Qin, Rui, 2024. "Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power," Applied Energy, Elsevier, vol. 358(C).
    10. Jian Yan & Zhuoxi Li & Xianyang Zhang, 2022. "Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testing," Papers 2210.08149, arXiv.org, revised Aug 2025.
    11. Han Lin Shang & Kaiying Ji & Ufuk Beyaztas, 2021. "Granger causality of bivariate stationary curve time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 626-635, July.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates

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