Granger non-causality 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 non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G- non-causality to test for structural non-causality 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.
Granger causality, Structural causality, Structural heterogeneity, Distributional heterogeneity, Cross-section, Panel data
Singapore Management University, School of Economics. Working Paper Series, Paper No. 04-2016
SMU Economics and Statistics Working Paper Series, Paper No. 04-2016
City or Country
LU, Xun; SU, Liangjun; and WHITE, Halbert.
Granger Causality and Structural Causality in Cross-Section and Panel Data. (2016). Singapore Management University, School of Economics. Working Paper Series, Paper No. 04-2016. 1-25. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1788
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.