Learning Causal Relations in Multivariate Time Series Data

43 Pages Posted: 18 Dec 2010

See all articles by Pu Chen

Pu Chen

Bielefeld University - Department of Business Administration and Economics

Hsiao Chihying

affiliation not provided to SSRN

Date Written: 2007

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. --

Keywords: Automated Learning, Bayesian Network, Inferred Causation, VAR, Wage-Price Spiral

JEL Classification: C1

Suggested Citation

Chen, Pu and Chihying, Hsiao, Learning Causal Relations in Multivariate Time Series Data (2007). Economics: The Open-Access, Open-Assessment E-Journal, Vol. 1, 2007-11, Available at SSRN: https://ssrn.com/abstract=1726790 or http://dx.doi.org/10.5018/economics-ejournal.ja.2007-11

Pu Chen (Contact Author)

Bielefeld University - Department of Business Administration and Economics ( email )

P.O. Box 100131
D-33501 Bielefeld, NRW 33501
Germany

Hsiao Chihying

affiliation not provided to SSRN

No Address Available