Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 150, Issue 2, Pages 228-237Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jneumeth.2005.06.011
Keywords
granger causality; conditional granger causality; multiple time series; frequency domain; multivariate autoregressive (MVAR) model; autore-gressive; moving average (ARMA) process; partition matrix
Categories
Funding
- NIMH NIH HHS [MH71620, MH64204, MH070498] Funding Source: Medline
Ask authors/readers for more resources
It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method. (c) 2005 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available