Graphical Gaussian modelling of multivariate time series with latent variables


Michael Eichler ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:193-200, 2010.


In time series analysis, inference about cause-effect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the identification of causal relationships is the possible presence of latent variables that affect the measured components and thus lead to so-called spurious causalities. In this paper, we describe a new graphical approach for modelling the dependence structure of multivariate stationary time series that are affected by latent variables. To this end, we introduce dynamic maximal ancestral graphs (dMAGs), in which each time series is represented by a single vertex. For Gaussian processes, this approach leads to vector autoregressive models with errors that are not independent but correlated according to the dashed edges in the graph. We discuss identifiability of the parameters and show that these models can be viewed as graphical ARMA models that satisfy the Granger causality restrictions encoded by the associated dynamic maximal ancestral graph.

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