VARX simulation
This function generates a simulated multivariate VAR time series.
simulateVARX(N, K, p, m, nobs, rho, sparsityA1, sparsityA2, sparsityA3, mu, method, covariance, ...)
N
: dimension of the time series.K
: TODOp
: number of lags of the VAR model.m
: TODOnobs
: number of observations to be generated.rho
: base value for the covariance matrix.sparsityA1
: density (in percentage) of the number of nonzero elements of the A1 block.sparsityA2
: density (in percentage) of the number of nonzero elements of the A2 block.sparsityA3
: density (in percentage) of the number of nonzero elements of the A3 block.mu
: a vector containing the mean of the simulated process.method
: which method to use to generate the VAR matrix. Possible values are "normal"
or "bimodal"
.covariance
: 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