A time domain frequency-selective multivariate granger causality approach

2016 ◽  
Author(s):  
Lutz Leistritz ◽  
Herbert Witte
Keyword(s):  
Granger Causality ◽  
Time Domain ◽  
Frequency Selective

scholarly journals Ability of Granger Causality Analysis to Detect Indirect Links: A Simulation Study

2020 ◽  
Vol 23 (2) ◽  
pp. 121-124
Author(s):  
N. W. Falasca ◽  
R. Franciotti
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Domain ◽  
Simulated Data ◽  
Biological Data ◽  
Connection Strength ◽  
Connectivity Analysis ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Virtual Time

Granger causality (G-causality) has emerged as a useful tool to investigate the influence that one system can exert over another system, but challenges remain when applying it to biological data. Specifically, it is not clear if G-causality can distinguish between direct and indirect influences. In this study time domain G-causality connectivity analysis was performed on simulated electroencephalographic cerebral signals. Conditional multivariate autoregressive model was applied to 19 virtual time series (nodes) to identify the effects of direct and indirect links while varying one of the following variables: the length of the time series, the lags between interacting nodes, the connection strength of the links, and the noise. Simulated data revealed that weak indirect influences are not identified by Gcausality analysis when applied on covariance stationary, non-correlated electrophysiological time series.

Time domain and frequency domain Granger causality networks: Application to China’s financial institutions

2020 ◽  
pp. 101662
Author(s):  
Gang-Jin Wang ◽  
Hui-Bin Si ◽  
Yang-Yang Chen ◽  
Chi Xie ◽  
Julien Chevallier
Keyword(s):  
Frequency Domain ◽  
Granger Causality ◽  
Financial Institutions ◽  
Time Domain
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