varma: simulation of multivariate ARMA process
In multiwave: Estimation of Multivariate Long-Memory Models Parameters

Description Usage Arguments Value Author(s) References See Also Examples

generates N observations of a k-vector ARMA process

1	varma(N, k = 1, VAR = NULL, VMA = NULL, cov_matrix = diag(k), innov=NULL)

`N`	number of time points.
`k`	dimension of the vector ARMA (optional, default is univariate)
`VAR`	array of VAR coefficient matrices (optional).
`VMA`	array of VMA coefficient matrices (optional).
`cov_matrix`	matrix of correlation between the innovations (optional, default is identity).
`innov`	matrix of the innovations (optional, default is a gaussian process).

vector containing the N observations of the k-vector ARMA process.

S. Achard and I. Gannaz

S. Achard, I. Gannaz (2016) Multivariate wavelet Whittle estimation in long-range dependence. Journal of Time Series Analysis, Vol 37, N. 4, pages 476-512. http://arxiv.org/abs/1412.0391.

S. Achard, I Gannaz (2019) Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave. Journal of Statistical Software, Vol 89, N. 6, pages 1-31.

fivarma, vfracdiff

rho1 <- 0.3
rho2 <- 0.8
cov <- matrix(c(1,rho1,rho2,rho1,1,rho1,rho2,rho1,1),3,3)

J <- 9
N <- 2^J
VMA <- diag(c(0.4,0.1,0))
### or another example VAR <- array(c(0.8,0,0,0,0.6,0,0,0,0.2,0,0,0,0,0.4,0,0,0,0.5),dim=c(3,3,2))
VAR <- diag(c(0.8,0.6,0))
x <- varma(N, k=3, cov_matrix=cov, VAR=VAR, VMA=VMA)