Learning Causal Relations in Multivariate Time Series Data

Pu Chen and Chih-Ying Hsiao ()

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), 2007, vol. 1, No 2007-11, 43 pages

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 (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.5018/economics-ejournal.ja.2007-11
https://www.econstor.eu/bitstream/10419/18009/1/economics_2007-11.pdf (application/pdf)

Related works:
Working Paper: Learning Causal Relations in Multivariate Time Series Data (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifweej:6175

DOI: 10.5018/economics-ejournal.ja.2007-11

Access Statistics for this article

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020) is currently edited by Dennis J. Snower

More articles in Economics - The Open-Access, Open-Assessment E-Journal (2007-2020) from Kiel Institute for the World Economy (IfW Kiel) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().