Testing Granger non-causality in Expectiles

Taamouti, Abderrahim ORCID: 0000-0002-1360-8803, Doukali, Mohamed ORCID: 0000-0001-9315-2326 and Taoufik, Bouezmarni (2023) Testing Granger non-causality in Expectiles. Econometric Reviews, 43 (1). pp. 30-51.

Text
Manuscript.pdf - Author Accepted Manuscript
Download (711kB) | Preview

Official URL: http://dx.doi.org/10.1080/07474938.2023.2246823

Abstract

This article aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are defined as least squares analog of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a sufficient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good finite-sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests.

Item Type:	Article
Uncontrolled Keywords:	Asymmetric loss function, expectile regression function, Granger causality in distribution, Granger causality in expectiles, sup-Wald test, C12, C22
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	28 Jul 2023 15:25
Last Modified:	11 Dec 2023 02:57
DOI:	10.1080/07474938.2023.2246823
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171988