Granger-Geweke causality: Estimation and interpretation


    loading  Checking for direct PDF access through Ovid

Abstract

In a recent PNAS article1, Stokes and Purdon performed numerical simulations to argue that Granger-Geweke causality (GGC) estimation is severely biased, or of high variance, and GGC application to neuroscience is problematic because the GGC measure is independent of ‘receiver’ dynamics. Here, we use the same simulation examples to show that GGC measures, when properly estimated either via the spectral factorization-enabled nonparametric approach or the VAR-model based parametric approach, do not have the claimed bias and high variance problems. Further, the receiver-independence property of GGC does not present a problem for neuroscience applications. When the nature and context of experimental measurements are taken into consideration, GGC, along with other spectral quantities, yield neurophysiologically interpretable results.HighlightsGGC (Geweke-Granger causality) can be reliably done with the non-para and parametric approaches.The reliability of GGC is illustrated with simulation examples.‘Receiver’-independence of GGC is not problematic for neuroscience applications.

    loading  Loading Related Articles