Description Usage Arguments Value Author(s) References See Also Examples
generates N observations of a k-vector ARMA process
1 |
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.
1 2 3 4 5 6 7 8 9 10 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.