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The Relation of Different Concepts of Causality in Econometrics

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  • Michael Lechner

Abstract

Granger and Sims non-causality (GSNC) are compared to non-causality based on concepts popular in the microeconometrics and programme evaluation literature (potential outcome non-causality, 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 to observable outcomes. Based on a dynamic model of potential outcomes, we find that in general neither of the concepts implies each other without further assumptions. However, identifying assumptions of the sequential selection on observable type provide the link between those concepts, such that GSNC implies PONC, and vice versa.

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  • Michael Lechner, 2006. "The Relation of Different Concepts of Causality in Econometrics," University of St. Gallen Department of Economics working paper series 2006 2006-15, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2006:2006-15
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    References listed on IDEAS

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    3. J. Paul Dunne & Ron Smith, 2010. "Military Expenditure And Granger Causality: A Critical Review," Defence and Peace Economics, Taylor & Francis Journals, vol. 21(5-6), pages 427-441.

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    More about this item

    Keywords

    Granger causality; Sims causality; Rubin causality; potential outcome model; dynamic treatments;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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