varma {multiwave}

simulation of multivariate ARMA process

Description

generates N observations of a k-vector ARMA process

Usage

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

Arguments

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).

Value

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

Author(s)

S. Achard and I. Gannaz

References

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.

See Also

fivarma, vfracdiff

Examples

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)