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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