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The Relation of Different Concepts of Causality in Econometrics

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  • Michael Lechner

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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.

Suggested Citation

  • 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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    1. Hosoya, Yuzo, 1977. "On the Granger Condition for Non-Causality," Econometrica, Econometric Society, vol. 45(7), pages 1735-1736, October.
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    5. Dufour, Jean-Marie & Tessier, David, 1993. "On the relationship between impulse response analysis, innovation accounting and Granger causality," Economics Letters, Elsevier, vol. 42(4), pages 327-333.
    6. Florens, J P & Mouchart, M, 1982. "A Note on Noncausality," Econometrica, Econometric Society, vol. 50(3), pages 583-591, May.
    7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    8. James J. Heckman, 2000. "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, Oxford University Press, vol. 115(1), pages 45-97.
    9. Michael Lechner, 2006. "Matching Estimating of Dynamic Treatment Models: Some Practical Issues," University of St. Gallen Department of Economics working paper series 2006 2006-03, Department of Economics, University of St. Gallen.
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    13. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
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    Cited by:

    1. 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.

    More about this item

    Keywords

    Granger causality; Sims causality; Rubin causality; potential outcome model; dynamic treatments;

    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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