Semiparametric detection of nonlinear causal coupling using partial directed coherence

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Abstract
Infering causal relationships from observed time series has attracted much recent attention. In cases of nonlinear coupling, adequate inference is often hindered by the need to specify coupling details that call for many parameters and global minimization of nonconvex functions. In this paper we use an example to investigate a new concept, termed here running entropy mapping, whereby time series are mapped onto other entropy related time sequences whose analysis via a linear parametric time series methods, such as partial directed coherence, is able to expose the presence of formerly linearly undetectable causal relationships.

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