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GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

Published online by Cambridge University Press:  17 March 2016

Xun Lu ,
Liangjun Su  and
Halbert White
Xun Lu*
Affiliation:
Hong Kong University of Science and Technology
Liangjun Su
Affiliation:
Singapore Management University
Halbert White
Affiliation:
University of California, San Diego
*
*Address correspondence to Xun Lu, Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong; e-mail: xunlu@ust.hk.
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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.

Type
ET LECTURE
Information
Econometric Theory , Volume 33 , Issue 2 , April 2017 , pp. 263 - 291
Copyright
Copyright © Cambridge University Press 2016 

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