A time series causal model
Pu Chen ()
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Inferred Causation; Automated Learning; VAR; Granger Causality; Wage-Price Spiral (search for similar items in EconPapers)
JEL-codes: E31 C01 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:24841
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