Graphical modelling of multivariate time series

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Abstract

We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context. Examples for graphical time series models include nonlinear autoregressive models and multivariate ARCH models.

Original languageEnglish
Pages (from-to)233-268
Number of pages36
JournalProbability Theory and Related Fields
Volume153
Issue number1-2
DOIs
Publication statusPublished - Jun 2012

Keywords

  • Graphical models
  • Multivariate time series
  • Granger causality
  • Global Markov property
  • CONDITIONAL-INDEPENDENCE
  • GEOMETRIC ERGODICITY
  • GENERALIZED ARCH
  • PATH DIAGRAMS
  • CAUSALITY
  • SYSTEMS
  • COHERENCE
  • GRANGER

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