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Learning Causal Relations in Multivariate Time Series Data

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  • Chen, Pu
  • Chihying, Hsiao

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.

Suggested Citation

  • 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.
  • Handle: RePEc:zbw:ifweej:6175
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    File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2007-11
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    References listed on IDEAS

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    1. Ellen R. Rissman, 1995. "Sectoral wage growth and inflation," Economic Perspectives, Federal Reserve Bank of Chicago, issue Jul, pages 15-28.
    2. Glymour, Clark & Spirtes, Peter, 1988. "Latent variables, causal models and overidentifying constraints," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 175-198.
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    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. Daniel Aaronson, 2001. "Price Pass-Through And The Minimum Wage," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 158-169, February.
    6. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
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    10. 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.
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    Citations

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    Cited by:

    1. Adriatik Hoxha, 2016. "The Wage-Price Setting Behavior: Comparing The Evidence from EU28 and EMU," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 19(60), pages 61-102, June.
    2. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.
    3. Lei Wu & Qingbin Meng & Kuan Xu, 2015. "'Slow-burn' spillover and 'fast and furious' contagion: a study of international stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 933-958, June.
    4. Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.
    5. Pu Chen & Chih-Ying Hsiao, 2010. "Causal Inference for Structural Equations: With an Application to Wage-Price Spiral," Computational Economics, Springer;Society for Computational Economics, vol. 36(1), pages 17-36, June.
    6. Adriatik Hoxha, 2016. "The Switch to Near-Rational Wage-Price Setting Behaviour: The Case of United Kingdom," EuroEconomica, Danubius University of Galati, issue 1(35), pages 127-148, may.

    More about this item

    Keywords

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

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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