Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. Chen, Y., Bressler, S., & Ding, M. J. Neurosci. Methods, 150:228--237, Jan, 2006.
abstract   bibtex   
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.
@article{ Chen_etal06,
  author = {Chen, Y. and Bressler, S.L. and Ding, M.},
  title = {{{F}requency decomposition of conditional {G}ranger causality and
	application to multivariate neural field potential data}},
  journal = {J. Neurosci. Methods},
  year = {2006},
  volume = {150},
  pages = {228--237},
  month = {Jan},
  abstract = {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.}
}
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