Directed Interaction Tests for Time-Series Analysis Based on VAR Model

Exploring directed influence relationships at different temporal and spatial scales is an important issue in time-series research. This paper develops a method for testing the directed interactions of multivariable time-series with a vector autoregressive (VAR) model. The calculation of Granger causality between the reference time-series and the other time-series is not rely on a priori specification of a model for pre-selected time-series, but aims at testing or contrasting specific hypotheses about time-series interactions. The measurement error interference on parameter estimates were evaluated by using VAR modeling, and then Granger causality relationships of time-series were detected in computational simulations. The simulation results demonstrate that the proposed method has a satisfactory performance on analyze directed interactions, when its applicability and usefulness are tested using multiple units of time-series.

[1]  Snigdhansu Chatterjee,et al.  Causality and pathway search in microarray time series experiment , 2007, Bioinform..

[2]  Hui Liu,et al.  Human activities and global warming: a cointegration analysis , 2005, Environ. Model. Softw..

[3]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[4]  Alexandre G. Patriota,et al.  Vector autoregressive models with measurement errors for testing Granger causality , 2009, 0911.5628.

[5]  Qiang Xu,et al.  Small-world directed networks in the human brain: Multivariate Granger causality analysis of resting-state fMRI , 2011, NeuroImage.

[6]  Wei Liao,et al.  Nonlinear connectivity by Granger causality , 2011, NeuroImage.

[7]  João Ricardo Sato,et al.  Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method , 2007, Bioinform..

[8]  R. Goebel,et al.  Investigating directed influences between activated brain areas in a motor-response task using fMRI. , 2006, Magnetic resonance imaging.

[9]  Carlos E. Thomaz,et al.  Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for fMRI data analysis , 2010, NeuroImage.

[10]  João Ricardo Sato,et al.  Modeling gene expression regulatory networks with the sparse vector autoregressive model , 2007, BMC Systems Biology.

[11]  Dongchu Sun,et al.  Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models , 2003 .

[12]  Anil K. Seth,et al.  Distinguishing Causal Interactions in Neural Populations , 2007, Neural Computation.