var_to_tsdata
Generate random multi-trial Gaussian VAR time series
Syntax
[X,E,mtrunc] = var_to_tsdata(A,SIG,m,N,mtrunc,decayfac)
Arguments
See also Common variable names and data structures.
input
A VAR coefficients matrix SIG residuals covariance matrix m number of observations per trial N number of trials (default: 1) mtrunc number of initial time observations to truncate or (default) empty for automatic calculation decayfac initial transients decay factor (default: 100)
output
X multi-trial Gaussian VAR time series E residuals time series mtrunc actual number of initial time steps truncated
Description
Return N time series of length m sampled from a VAR model with coefficients matrix A, and iid Gaussian residuals with covariance matrix SIG:
If mtrunc is supplied it is taken to be the the number of initial (non-stationary transient) observations to truncate; otherwise (default) the spectral radius of A (see function var_specrad) is calculated and used to estimate a suitable number mtrunc of observations to assumed stationarity (roughly till autocovariance decays to near zero). Set decayfac larger for longer settle time.
References
[1] L. Barnett and A. K. Seth, The MVGC Multivariate Granger Causality Toolbox: A New Approach to Granger-causal Inference, J. Neurosci. Methods 223, 2014 [ preprint ].