Granger-Causality Maps of Diffusion Processes

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00457886" target="_blank" >RIV/67985807:_____/16:00457886 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1103/PhysRevE.93.022213" target="_blank" >http://dx.doi.org/10.1103/PhysRevE.93.022213</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevE.93.022213" target="_blank" >10.1103/PhysRevE.93.022213</a>

Result language
angličtina
Original language name
Granger-Causality Maps of Diffusion Processes
Original language description
Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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