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

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