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A time series causal model

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  • Chen, Pu

Abstract

Cause-effect relations are central in economic analysis. Uncovering empirical cause-effect relations is one of the main research activities of empirical economics. In this paper we develop a time series casual model to explore casual relations among economic time series. The time series causal model is grounded on the theory of inferred causation that is a probabilistic and graph-theoretic approach to causality featured with automated learning algorithms. Applying our model we are able to infer cause-effect relations that are implied by the observed time series data. The empirically inferred causal relations can then be used to test economic theoretical hypotheses, to provide evidence for formulation of theoretical hypotheses, and to carry out policy analysis. Time series causal models are closely related to the popular vector autoregressive (VAR) models in time series analysis. They can be viewed as restricted structural VAR models identified by the inferred causal relations.

Suggested Citation

  • Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:24841
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    File URL: https://mpra.ub.uni-muenchen.de/24841/1/MPRA_paper_24841.pdf
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    References listed on IDEAS

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    1. 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.
    2. Glymour, Clark & Spirtes, Peter, 1988. "Latent variables, causal models and overidentifying constraints," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 175-198.
    3. James M. Robins, 2003. "Uniform consistency in causal inference," Biometrika, Biometrika Trust, vol. 90(3), pages 491-515, September.
    4. Krolzig, Hans-Martin & Peter Flaschel, 2003. "Wage and Price Phillips Curves," Royal Economic Society Annual Conference 2003 128, Royal Economic Society.
    5. 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, vol. 10(4), pages 1-35, December.
    6. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
    7. 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.
    8. Swanson, N.R. & Granger, C.W.J., 1994. "Impulse Response Functions Based on Causal Approach to Residual Orthogonalization in Vector Autoregressions," Papers 9-94-1, Pennsylvania State - Department of Economics.
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    Citations

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    Cited by:

    1. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    2. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.

    More about this item

    Keywords

    Inferred Causation; Automated Learning; VAR; Granger Causality; Wage-Price Spiral;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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