This function generates a simulated multivariate VAR time series.
simulate_var(n, p, nobs, rho, sparsity, mu, method, covariance, ...)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
dimension of the time series (default n = 100).
number of lags of the VAR model (default p = 1).
number of observations to be generated (default
nobs = 250).
base value for the covariance matrix (default rho = 0.5).
density (in percentage) of the number of nonzero elements
of the VAR matrices (default sparsity = 0.05).
a vector containing the mean of the simulated process (default
mu = 0).
which method to use to generate the VAR matrix. Possible values
are "normal" or "bimodal" ((default method = "normal")).
type of covariance matrix to use in the simulation.
Possible values: "Toeplitz", "block1", "block2",
"Wishart" or simply "diagonal"
(default covariance = "Toeplitz").
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;