The inference of causal interaction structures in multivariate systems enables a deeper understanding of the investigated network. Analyzing nonlinear systems using partial directed coherence requires high model orders of the underlying vector-autoregressive process. We present a method to overcome the drawbacks caused by the high model orders. We calculate the corresponding statistics and provide a significance level. The performance is illustrated by means of model systems and in an application to neurological data.
Sommerlade, L., Eichler, M., Jachan, M., Henschel, K., Timmer, J., & Schelter, B. (2009). Estimating causal dependencies in networks of nonlinear stochastic dynamical systems. Physical Review E, 80, 1-9. https://doi.org/10.1103/PhysRevE.80.051128