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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: LU, Xun, SU, Liangjun, WHITE, Halbert
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Online Access:https://ink.library.smu.edu.sg/soe_research/1788
https://ink.library.smu.edu.sg/context/soe_research/article/2787/viewcontent/04_2016.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soe_research-2787
record_format dspace
spelling sg-smu-ink.soe_research-27872019-04-20T02:55:14Z Granger Causality and Structural Causality in Cross-Section and Panel Data LU, Xun SU, Liangjun WHITE, Halbert 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. 2016-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1788 https://ink.library.smu.edu.sg/context/soe_research/article/2787/viewcontent/04_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Granger causality Structural causality Structural heterogeneity Distributional heterogeneity Cross-section Panel data Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Granger causality
Structural causality
Structural heterogeneity
Distributional heterogeneity
Cross-section
Panel data
Econometrics
spellingShingle Granger causality
Structural causality
Structural heterogeneity
Distributional heterogeneity
Cross-section
Panel data
Econometrics
LU, Xun
SU, Liangjun
WHITE, Halbert
Granger Causality and Structural Causality in Cross-Section and Panel Data
description 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.
format text
author LU, Xun
SU, Liangjun
WHITE, Halbert
author_facet LU, Xun
SU, Liangjun
WHITE, Halbert
author_sort LU, Xun
title Granger Causality and Structural Causality in Cross-Section and Panel Data
title_short Granger Causality and Structural Causality in Cross-Section and Panel Data
title_full Granger Causality and Structural Causality in Cross-Section and Panel Data
title_fullStr Granger Causality and Structural Causality in Cross-Section and Panel Data
title_full_unstemmed Granger Causality and Structural Causality in Cross-Section and Panel Data
title_sort granger causality and structural causality in cross-section and panel data
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/soe_research/1788
https://ink.library.smu.edu.sg/context/soe_research/article/2787/viewcontent/04_2016.pdf
_version_ 1770572849134698496