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Learning Causal Relations in Multivariate Time Series Data

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  • Chihying, Hsiao
  • Chen, Pu

Abstract

Applying a probabilistic causal approach, we define a class of time series causal models (TSCM) based on stationary Bayesian networks. A TSCM can be seen as a structural VAR identified by the causal relations among the variables. We classify TSCMs into observationally equivalent classes by providing a necessary and sufficient condition for the observational equivalence. Applying an automated learning algorithm, we are able to consistently identify the data-generating causal structure up to the class of observational equivalence. In this way we can characterize the empirical testable causal orders among variables based on their observed time series data. It is shown that while an unconstrained VAR model does not imply any causal orders in the variables, a TSCM that contains some empirically testable causal orders implies a restricted SVAR model. We also discuss the relation between the probabilistic causal concept presented in TSCMs and the concept of Granger causality. It is demonstrated in an application example that this methodology can be used to construct structural equations with causal interpretations. --

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Bibliographic Info

Paper provided by Kiel Institute for the World Economy in its series Economics Discussion Papers with number 2007-15.

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Date of creation: 2007
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Handle: RePEc:zbw:ifwedp:5529

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Keywords: Automated Learning; Bayesian Network; Inferred Causation; VAR; Wage-Price Spiral;

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References

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  1. Pu Chen & Carl Chiarella & Peter Flaschel & Willi Semmler, 2006. "Keynesian Macrodynamics and the Phillips Curve. An Estimated Baseline Macromodel for the U.S. Economy," Working Paper Series 147, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
  2. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
  3. Glymour, Clark & Spirtes, Peter, 1988. "Latent variables, causal models and overidentifying constraints," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 175-198.
  4. Vincent Hogan, 1998. "Explaining the Recent Behavior of Inflation and Unemployment in the United States," IMF Working Papers 98/145, International Monetary Fund.
  5. 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.
  6. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, December.
  7. Douglas Staiger & James H. Stock & Mark W. Watson, 1997. "The NAIRU, Unemployment and Monetary Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 33-49, Winter.
  8. Jonsson, Magnus & Palmqvist, Stefan, 2004. "Do Higher Wages Cause Inflation?," Working Paper Series 159, Sveriges Riksbank (Central Bank of Sweden).
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Cited by:
  1. Pu Chen & Chih-Ying Hsiao, 2010. "Causal Inference for Structural Equations: With an Application to Wage-Price Spiral," Computational Economics, Society for Computational Economics, vol. 36(1), pages 17-36, June.
  2. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.
  3. Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.

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