Learning causal relations in multivariate time series data

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dc.contributor.author Chen, P
dc.contributor.author Hsiao, C
dc.date.accessioned 2012-02-02T11:03:47Z
dc.date.issued 2007-01
dc.identifier.citation Economics, 2007, 2007 (11), pp. 1 - 43
dc.identifier.issn 1864-6042
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
dc.format Scott McWhirter
dc.publisher E-Journal
dc.title Learning causal relations in multivariate time series data
dc.type Journal Article
dc.parent Economics
dc.journal.volume 11
dc.journal.volume 2007
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 140302 Econometric and Statistical Methods
dc.for 150202 Financial Econometrics
dc.for 1502 Banking, Finance and Investment
dc.personcode 997772
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
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Business
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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