Granger Causality and Structural Causality in Cross-Section and Panel Data

Abstract

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series

“…It is necessary to find a more appropriate and rigorous method to define the relationship between time series. In certain fields, like economics and neuroscience, Granger causality test has been widely used to measure the relationships between time series 22 – 25 . One of the advantages of Granger causality test is that it can statistically measure the extent to which one time series explains the change of another time series in the future 26 – 28 , and detect the directed links between time series.…”

Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series

“…Further to this it should be noted that the methods presented in this paper can be seen as generally agnostic to the method used to determine the interactions, so long as these results are directed and ranged between 0 and 1 (with 0 being no interaction). Granger causality is a convenient basis to demonstrate new methodologies due to its simplicity and familiarity in many fields (for example economics and neuroscience 13 , 45 , 51 , 52 ). Due to the popularity of Granger causality many extensions have been proposed 53 – 56 , however to allow this work to be as comprehensible as possible to a range of audiences we choose to employ the classic formulation, as is used by Jiang et al in their similar work, and which is sufficient for the task required of it.…”

Women on boards and corporate social irresponsibility: evidence from a Granger style reverse causality minimisation procedure

“…To address this problem, we propose a Granger-style reverse causality minimisation procedure. While Granger (1969Granger ( , 1980Granger ( , 1988 causality was established decades ago, it remains a very important concept that can be suitable in both a time series (White and Lu 2010) and a cross sectional/panel regression framework (Lu, Su, and White 2017). In addition to using Granger causality to test for the presence of reverse causality, our procedure is inspired by an increased use of orthogonalization as a means of eliminating unwanted effects from variables (e.g.…”