Relationship Between Granger Noncausality and Network Graph of State-Space RepresentationsJozsa, M., Petreczky, M. & Camlibel, M. K., Mar-2019, In : IEEE Transactions on Automatic Control. 64, 3, p. 912-927 16 p.
Research output: Contribution to journal › Article › Academic › peer-review
The goal of this paper is to explore the relationship between the network graph of a state-space representation of an observed process and the causal relations among the components of that process. We will show that the existence of a linear time-invariant state-space representation, with its network graph being the star graph, is equivalent to (conditional) Granger noncausal relations among the components of the output process. Granger non-causality is a statistical concept, which applies to arbitrary processes and does not depend on the representation of the process. That is, we relate intrinsic properties of a process with the network graph of its state-space representations.
|Number of pages||16|
|Journal||IEEE Transactions on Automatic Control|
|Publication status||Published - Mar-2019|
- Interconnected systems, stochastic systems, system realization, MODEL-REDUCTION, TIME-SERIES, BAYES NETS, CAUSALITY, IDENTIFICATION, REALIZATIONS