أعرض تسجيلة المادة بشكل مبسط

dc.creator Chihying, Hsiao
dc.creator Chen, Pu
dc.date 2007
dc.date.accessioned 2013-10-16T06:57:30Z
dc.date.available 2013-10-16T06:57:30Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/17938
dc.identifier ppn:558074707
dc.identifier RePEc:zbw:ifwedp:5529
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/17938
dc.description 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.language eng
dc.publisher Kiel Institute for the World Economy (IfW) Kiel
dc.relation Economics Discussion Papers / Institut für Weltwirtschaft 2007-15
dc.rights http://creativecommons.org/licenses/by-nc/2.0/de/deed.en
dc.subject C1
dc.subject ddc:330
dc.subject Automated Learning
dc.subject Bayesian Network
dc.subject Inferred Causation
dc.subject VAR
dc.subject Wage-Price Spiral
dc.title Learning Causal Relations in Multivariate Time Series Data
dc.type doc-type:workingPaper


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أعرض تسجيلة المادة بشكل مبسط