Graphical models for multivariate Markov chains
The aim of this paper is to provide a graphical representation of the dynamic relations among the marginal processes of a first order multivariate Markov chain. We show how to read Granger-noncausal and contemporaneous independence relations off a particular type of mixed graph, when directed and bi-directed edges are missing. Insights are also provided into the Markov properties with respect to a graph that are retained under marginalization of a multivariate chain. Multivariate logistic models for transition probabilities are associated with the mixed graphs encoding the relevant independencies. Finally, an application on real data illustrates the methodology.
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Volume (Year): 107 (2012)
Issue (Month): C ()
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References listed on IDEAS
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- Joseph B. Lang, 2005. "Homogeneous Linear Predictor Models for Contingency Tables," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 121-134, March.
- Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
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