A Better Understanding of Granger Causality Analysis: A Big Data Environment

51 Pages Posted: 11 Feb 2017

See all articles by Xiaojun Song

Xiaojun Song

Peking University - Guanghua School of Management

Abderrahim Taamouti

Durham University Business School

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Date Written: February 10, 2017

Abstract

We provide a better understanding of the causal structure in a multivariate time series by introducing a novel statistical procedure 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. We propose an efficient statistical procedure to test for the presence of indirect/spurious causality based on big data analysis. Furthermore, we suggest an identification procedure to find the variables that transmit/induce the indirect/spurious causality. Finally, we provide an empirical application where 135 economic variables were used to study a possible indirect causality from money/credit to income.

Keywords: Indirect causality, spurious causality, big data analysis, auxiliary variable(s)

JEL Classification: C12, C32, C38, C53, E60

Suggested Citation

Song, Xiaojun and Taamouti, Abderrahim, A Better Understanding of Granger Causality Analysis: A Big Data Environment (February 10, 2017). Available at SSRN: https://ssrn.com/abstract=2914997 or http://dx.doi.org/10.2139/ssrn.2914997

Xiaojun Song (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Abderrahim Taamouti

Durham University Business School ( email )

Mill Hill Lane
Durham, DH1 3LB
United Kingdom

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