Looking behind Granger causality
AbstractGranger causality as a popular concept in time series analysis is widely applied in empirical research. The interpretation of Granger causality tests in a cause-effect context is, however, often unclear or even controversial, so that the causality label has faded away. Textbooks carefully warn that Granger causality does not imply true causality and preferably refer the Granger causality test to a forecasting technique. Applying theory of inferred causation, we develop in this paper a method to uncover causal structures behind Granger causality. In this way we re-substantialize the causal attribution in Granger causality through providing an causal explanation to the conditional dependence manifested in Granger causality.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 24859.
Date of creation: Sep 2010
Date of revision:
Granger Causality; Time Series Causal Model; Graphical Model;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-09-18 (All new papers)
- NEP-ECM-2010-09-18 (Econometrics)
- NEP-ETS-2010-09-18 (Econometric Time Series)
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- Chen, Pu & Chihying, Hsiao, 2007.
"Learning Causal Relations in Multivariate Time Series Data,"
Economics - The Open-Access, Open-Assessment E-Journal,
Kiel Institute for the World Economy, vol. 1(11), pages 1-43.
- Chihying, Hsiao & Chen, Pu, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics Discussion Papers 2007-15, Kiel Institute for the World Economy.
- Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, Elsevier, vol. 137(2), pages 334-353, April.
- Krolzig, Hans-Martin & Peter Flaschel, 2003. "Wage and Price Phillips Curves," Royal Economic Society Annual Conference 2003, Royal Economic Society 128, Royal Economic Society.
- Chen Pu & Flaschel Peter, 2006. "Measuring the Interaction of Wage and Price Phillips Curves for the U.S. Economy," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 10(4), pages 1-35, December.
- Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(01), pages 69-77, February.
- James M. Robins, 2003. "Uniform consistency in causal inference," Biometrika, Biometrika Trust, Biometrika Trust, vol. 90(3), pages 491-515, September.
- Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.
- Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 2(1), pages 329-352, May.
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