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The Relation of Different Concepts of Causality Used in Time Series and Microeconometrics

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  • Michael Lechner

Abstract

Granger and Sims noncausality (GSNC), a concept frequently applied in time series econometrics, is compared to noncausality based on concepts popular in microeconometrics, program evaluation, and epidemiology literature (potential outcome noncausality or PONC). GSNC is defined as a set of restrictions on joint distributions of random variables with observable sample counterparts, whereas PONC combines restrictions on partially unobservable variables (potential outcomes) with different identifying assumptions that relate potential outcome variables to their observable counterparts. Based on the Robins' dynamic model of potential outcomes, we find that in general neither of the concepts implies each other without further (untestable) assumptions. However, the identifying assumptions associated with the sequential selection of the observables link these concepts such that GSNC implies PONC, and vice versa.

Suggested Citation

  • Michael Lechner, 2011. "The Relation of Different Concepts of Causality Used in Time Series and Microeconometrics," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 109-127.
    • Handle: RePEc:taf:emetrv:v:30:y:2011:i:1:p:109-127
    DOI: 10.1080/07474938.2011.520571
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    References listed on IDEAS

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    1. Ruth Miquel, 2002. "Identification of Dynamic Treatment Effects by Instrumental Variables," University of St. Gallen Department of Economics working paper series 2002 2002-11, Department of Economics, University of St. Gallen.
    2. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
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    2. Matthieu Droumaguet & Anders Warne & Tomasz Wozniak, 2015. "Granger Causality and Regime Inference in Bayesian Markov-Switching VARs," Department of Economics - Working Papers Series 1191, The University of Melbourne.
    3. Ashesh Rambachan & Neil Shephard, 2019. "When do common time series estimands have nonparametric causal meaning?," Papers 1903.01637, arXiv.org, revised Jan 2025.
    4. Antonio Musolesi & Hervé Cardot, 2017. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," Working Papers 2017036, University of Ferrara, Department of Economics.
    5. Matthieu Droumaguet & Tomasz Wozniak, 2012. "Bayesian Testing of Granger Causality in Markov-Switching VARs," Economics Working Papers ECO2012/06, European University Institute.
    6. Das, Aniruddha, 2022. "Religious attendance and global cognitive function: A fixed-effects cross-lagged panel modeling study of older U.S. adults," Social Science & Medicine, Elsevier, vol. 292(C).

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