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The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

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posted on 2023-06-08, 17:53 authored by Lionel BarnettLionel Barnett, Anil SethAnil Seth
Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, e?ect. It is de?ned in both time and frequency domains, and allows for the conditioning out of common causal in?uences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational e?ciency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a ?exible, powerful and e?cient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysis

Funding

Fellowship; G/700543/1; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL

History

Publication status

  • Published

File Version

  • Submitted version

Journal

Journal of Neuroscience Methods

ISSN

0165-0270

Publisher

Elsevier

Volume

223

Page range

50-68

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2014-07-21

First Open Access (FOA) Date

2014-07-21

First Compliant Deposit (FCD) Date

2014-07-21

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