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A review of the Granger-causality fallacy

Author

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  • Mariusz Maziarz

    () (Warsaw School of Economics)

Abstract

Methods used to infer causal relations from data rather than knowledge of mechanisms are most helpful and exploited only if the theoretical background is insufficient or experimentation impossible. The review of literature shows that when an investigator has no prior knowledge of the researched phenomenon, no result of the Granger-causality test has any epistemic utility due to different possible interpretations. (1) Rejecting the null in one of the tests can be interpreted as either a true causal relation, opposite direction of the true causation, instant causality, time series cointegration, not frequent enough sampling, etc. (2) Bi-directional Granger causality can be read either as instant causality or common cause fallacy. (3) Non-rejection of both nulls possibly means either indirect or nonlinear causality, or no causal relation.

Suggested Citation

  • Mariusz Maziarz, 2015. "A review of the Granger-causality fallacy," The Journal of Philosophical Economics, Bucharest Academy of Economic Studies, The Journal of Philosophical Economics, vol. 8(2), May.
  • Handle: RePEc:bus:jphile:v:8:y:2015:i:2:n:6
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    References listed on IDEAS

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    1. Hsiu-Yun Lee & Kenneth Lin & Jyh-Lin Wu, 2002. "Pitfalls in using Granger causality tests to find an engine of growth," Applied Economics Letters, Taylor & Francis Journals, vol. 9(6), pages 411-414.
    2. Sargent, Thomas J, 1976. "A Classical Macroeconometric Model for the United States," Journal of Political Economy, University of Chicago Press, vol. 84(2), pages 207-237, April.
    3. 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.
    4. Geweke, John & Meese, Richard & Dent, Warren, 1983. "Comparing alternative tests of causality in temporal systems : Analytic results and experimental evidence," Journal of Econometrics, Elsevier, vol. 21(2), pages 161-194, February.
    5. Harvey, A. C. & Stock, James H., 1989. "Estimating integrated higher-order continuous time autoregressions with an application to money-income causality," Journal of Econometrics, Elsevier, vol. 42(3), pages 319-336, November.
    6. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    7. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    8. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.
    9. Conway, Roger K. & Swamy, P. A. V. B. & Yanagida, John F. & Muehlen, Peter von zur, 1984. "The Impossibility of Causality Testing," Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, issue 3.
    10. Glasure, Yong U. & Lee, Aie-Rie, 1998. "Cointegration, error-correction, and the relationship between GDP and energy: The case of South Korea and Singapore," Resource and Energy Economics, Elsevier, vol. 20(1), pages 17-25, March.
    11. repec:ags:ersaer:149081 is not listed on IDEAS
    12. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882.
    13. William Schwert, G., 1979. "Tests of causality : The message in the innovations," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 10(1), pages 55-96, January.
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    1. repec:bus:jphile:v:10:y:2017:i:2:n:2 is not listed on IDEAS
    2. repec:gam:jecomi:v:5:y:2017:i:3:p:22-:d:102241 is not listed on IDEAS

    More about this item

    Keywords

    Granger-causality; epistemology of causality; causality testing;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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