Chen, Pu (2010): A time series causal model.

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Abstract
Causeeffect relations are central in economic analysis. Uncovering empirical causeeffect 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 graphtheoretic approach to causality featured with automated learning algorithms. Applying our model we are able to infer causeeffect 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.
Item Type:  MPRA Paper 

Original Title:  A time series causal model 
Language:  English 
Keywords:  Inferred Causation, Automated Learning, VAR, Granger Causality, WagePrice Spiral 
Subjects:  E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E31  Price Level ; Inflation ; Deflation C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  24841 
Depositing User:  Pu Chen 
Date Deposited:  13. Sep 2010 12:21 
Last Modified:  15. Mar 2015 13:15 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/24841 