Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vectorautoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measureof time-directed information transfer between jointly dependent processes. We exploit such equivalence andcalculate exactly the local Granger causality, i.e., the profile of the information transferred from the driver tothe target process at each discrete time point; in this frame, GC is the average of its local version. We showthat the variability of the local GC around its mean relates to the interplay between driver and innovation(autoregressive noise) processes, and it may reveal transient instances of information transfer not detectablefrom its average values. Our approach offers a robust and computationally fast method to follow the informationtransfer along the time history of linear stochastic processes, as well as of nonlinear complex systems studied inthe Gaussian approximation.
|Numero di pagine||5|
|Rivista||PHYSICAL REVIEW. E|
|Stato di pubblicazione||Published - 2021|
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