A time series causal model
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.
|Date of creation:||Sep 2010|
|Date of revision:|
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- 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).
- 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.
- 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.
- Krolzig, Hans-Martin & Peter Flaschel, 2003. "Wage and Price Phillips Curves," Royal Economic Society Annual Conference 2003 128, Royal Economic Society.
- James M. Robins, 2003. "Uniform consistency in causal inference," Biometrika, Biometrika Trust, vol. 90(3), pages 491-515, September.
- 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.
- Glymour, Clark & Spirtes, Peter, 1988. "Latent variables, causal models and overidentifying constraints," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 175-198.
- Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
- 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.
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