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


  • Xun Lu

    (Department of Economics, Hong Kong University of Science and Technology)

  • Liangjun Su

    () (Singapore Management University)

  • Halbert White

    (University of California)


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 de nition 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 e¤ects are well de ned and identi able, G-non-causality follows from structural non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justi es 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.

Suggested Citation

  • Xun Lu & Liangjun Su & Halbert White, 2016. "Granger Causality and Structural Causality in Cross-Section and Panel Data," Working Papers 04-2016, Singapore Management University, School of Economics.
  • Handle: RePEc:siu:wpaper:04-2016

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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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