Looking behind Granger causality
Granger 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.
|Date of creation:||Sep 2010|
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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 (IfW), vol. 1, 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 (IfW).
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- Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.
- Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(01), pages 69-77, February. Full references (including those not matched with items on IDEAS)
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