A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion
| Autor: | Sessi Tokpavi, Bertrand Candelon |
|---|---|
| Přispěvatelé: | Laboratoire d'Économie d'Orleans [UMR7322] (LEO), Université d'Orléans (UO)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), EconomiX, Université Paris Nanterre (UPN)-Centre National de la Recherche Scientifique (CNRS), Macro, International & Labour Economics, RS: GSBE EFME |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Economics and Econometrics Multivariate statistics Financial contagion Monte Carlo method TIME-SERIES CONDITIONAL-INDEPENDENCE MARKETS Financial spillover Granger causality 0502 economics and business Statistics Econometrics Economics Kernel-based test 050207 economics ComputingMilieux_MISCELLANEOUS 050205 econometrics RISK Tails Estimation theory 05 social sciences Nonparametric statistics [SHS.ECO]Humanities and Social Sciences/Economics and Finance Conditional independence Statistics Probability and Uncertainty Null hypothesis Social Sciences (miscellaneous) |
| Zdroj: | Journal of Business and Economic Statistics Journal of Business and Economic Statistics, Taylor & Francis, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩ Journal of Business and Economic Statistics, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩ Journal of Business & Economic Statistics, 34(2), 240-253. Taylor and Francis |
| ISSN: | 1537-2707 0735-0015 |
| DOI: | 10.1080/07350015.2015.1026774 |
| Popis: | This article introduces a kernel-based nonparametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event. The main advantage of this test is its ability to examine a large number of lags, with higher-order lags discounted. In addition, our test is highly flexible because it can be used to identify Granger causality in specific regions on the distribution supports, such as the center or tails. We prove that the test converges asymptotically to a standard Gaussian distribution under the null hypothesis and thus is free of parameter estimation uncertainty. Monte Carlo simulations illustrate the excellent small sample size and power properties of the test. This new test is applied to a set of European stock markets to analyze spillovers during the recent European crisis and to distinguish contagion from interdependence effects. |
| Databáze: | OpenAIRE |
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