A better understanding of Granger causality analysis: A big data environment

Song, X.; Taamouti, A.

Abstract

This paper aims to provide a better understanding of the causal structure in a multivariate time series by introducing several statistical procedures for testing indirect and spurious causal effects. In practice, detecting these effects is a complicated task, since the auxiliary variables that transmit/induce indirect/spurious causality are very often unknown. The availability of hundreds of economic variables makes this task even more difficult since it is generally infeasible to find the appropriate auxiliary variables among all the available ones. In addition, including hundreds of variables and their lags in a regression equation is technically difficult. The paper proposes several statistical procedures to test for the presence of indirect/spurious causality based on big data analysis. Furthermore, it suggests an identification procedure to find the variables that transmit/induce the indirect/spurious causality. Finally, it provides an empirical application where 135 economic variables were used to study a possible indirect causality from money/credit to income.

Citation

Song, X., & Taamouti, A. (2019). A better understanding of Granger causality analysis: A big data environment. Oxford Bulletin of Economics and Statistics, 81(4), 911-936. https://doi.org/10.1111/obes.12288

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2018
• Online Publication Date: Dec 19, 2018
• Publication Date: Aug 31, 2019
• Deposit Date: Nov 9, 2018
• Publicly Available Date: Dec 19, 2020
• Journal: Oxford Bulletin of Economics and Statistics
• Print ISSN: 0305-9049
• Electronic ISSN: 1468-0084
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 81
• Issue: 4
• Pages: 911-936
• DOI: https://doi.org/10.1111/obes.12288
• Public URL: https://durham-repository.worktribe.com/output/1309502