Publication Type

Journal Article

Version

Postprint

Publication Date

4-2017

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 implies G-causality. This justifies using tests of G-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.

Keywords

Granger causality, Structural causality, Structural heterogeneity, Distributional heterogeneity, Cross-section, Panel data

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometric Theory

Volume

33

Issue

2

First Page

263

Last Page

291

ISSN

0266-4666

Identifier

10.1017/S0266466616000086

Publisher

Cambridge University Press

Embargo Period

4-24-2017

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1017/S0266466616000086

Included in

Econometrics Commons

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