Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/18009 
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dc.contributor.authorChen, Puen
dc.contributor.authorChihying, Hsiaoen
dc.date.accessioned2009-01-28T15:06:05Z-
dc.date.available2009-01-28T15:06:05Z-
dc.date.issued2007-
dc.identifier.citation|aEconomics: The Open-Access, Open-Assessment E-Journal|c1864-6042|v1|h2007-11|nKiel Institute for the World Economy (IfW)|lKiel|y2007|p1-43en
dc.identifier.pidoi:10.5018/economics-ejournal.ja.2007-11en
dc.identifier.urihttp://hdl.handle.net/10419/18009-
dc.description.abstractApplying 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
dc.language.isoengen
dc.publisher|aKiel Institute for the World Economy (IfW) |cKielen
dc.subject.jelC1en
dc.subject.ddc330en
dc.subject.keywordAutomated Learningen
dc.subject.keywordBayesian Networken
dc.subject.keywordInferred Causationen
dc.subject.keywordVARen
dc.subject.keywordWage-Price Spiralen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwKausalanalyseen
dc.subject.stwVAR-Modellen
dc.subject.stwStatistische Methodeen
dc.titleLearning Causal Relations in Multivariate Time Series Data-
dc.typeArticleen
dc.identifier.ppn540149861en
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.enen
dc.identifier.repecRePEc:zbw:ifweej:6175en
econstor.citation.journaltitleEconomics: The Open-Access, Open-Assessment E-Journalen
econstor.citation.issn1864-6042en
econstor.citation.volume1en
econstor.citation.issue2007-11en
econstor.citation.publisherKiel Institute for the World Economy (IfW)en
econstor.citation.publisherplaceKielen
econstor.citation.year2007en
econstor.citation.startpage1en
econstor.citation.endpage43en

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