This function generates a simulated multivariate VAR time series.
simulateVAR(N, p, nobs, rho, sparsity, mu, method, covariance, ...)dimension of the time series.
number of lags of the VAR model.
number of observations to be generated.
base value for the covariance matrix.
density (in percentage) of the number of nonzero elements of the VAR matrices.
a vector containing the mean of the simulated process.
which method to use to generate the VAR matrix. Possible values
are "normal" or "bimodal".
type of covariance matrix to use in the simulation. Possible
values: "toeplitz", "block1", "block2" or simply "diagonal".
the options for the simulation. These are:
muMat: the mean of the entries of the VAR matrices;
sdMat: the sd of the entries of the matrices;
A a list of NxN matrices ordered by lag
data a list with two elements: series the multivariate time series and
noises the time series of errors
S the variance/covariance matrix of the process