Learning causal relations in multivariate time series data

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dc.contributor.author Chen, Pu en_US
dc.contributor.author Hsiao, Chih-Ying en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T11:03:47Z
dc.date.available 2012-02-02T11:03:47Z
dc.date.issued 2007 en_US
dc.identifier 2009002736 en_US
dc.identifier.citation Chen Pu and Hsiao Chih-Ying 2007, 'Learning causal relations in multivariate time series data', E-Journal, vol. 2007, no. 11, pp. 1-43. en_US
dc.identifier.issn 1864-6042 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15875
dc.description.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 en_US
dc.language en_US
dc.publisher E-Journal en_US
dc.title Learning causal relations in multivariate time series data en_US
dc.parent Economics en_US
dc.journal.volume 2007 en_US
dc.journal.number 11 en_US
dc.publocation Germany en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 43 en_US
dc.cauo.name BUS.School of Finance and Economics en_US
dc.conference Verified OK en_US
dc.for 150200 en_US
dc.personcode 0000022208 en_US
dc.personcode 997772 en_US
dc.percentage 40 en_US
dc.classification.name Banking, Finance and Investment en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 997772 en_US


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