Year: 2011
Type: book
Abstract: This paper introduces a novel method called the ERR-Causality, or Error Reduction Ratio Causality test, that can be used to detect and track causal relationships \nbetween two signals using a new adaptive forward \northogonal least squares (Adaptive-Forward-OLS) algorithm. \nIn comparison to the traditional Granger method, \none advantage of the new ERR-Causality test is that it \ncan effectively detect the time-varying direction of linear \nor nonlinear causality between two signals without fitting \na complete model. Another important advantage is that \nthe ERR-Causality test can detect both the direction of \ninteractions and estimate the relative time shift between \nthe two signals. Several numerical examples are provided \nto illustrate the effectiveness of the new method for causal \nrelationship detection between two signals. An important \nreal application, relating to the analysis of the causality \nof EEG signals from different cortical sites which can be \nvery useful for understanding brain activity during an \nepileptic seizure by inspecting the high-resolution time varying directed information flow, is also discussed.
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2290080089