mww_cov_eval {multiwave} | R Documentation |
multivariate wavelet Whittle estimation of the long-run covariance matrix
Description
Computes the multivariate wavelet Whittle estimation of the long-run covariance matrix given the long-memory parameter vector d
, using DWTexact
for the wavelet decomposition.
Usage
mww_cov_eval(d, x, filter, LU)
Arguments
d |
vector of long-memory parameters (dimension should match dimension of x). |
x |
data (matrix with time in rows and variables in columns). |
filter |
wavelet filter as obtain with |
LU |
bivariate vector (optional) containing
|
Details
L
is fixing the lower limit of wavelet scales. L
can be increased to avoid finest frequencies that can be corrupted by the presence of high frequency phenomena.
U
is fixing the upper limit of wavelet scales. U
can be decreased when highest frequencies have to be discarded.
Value
long-run covariance matrix estimation.
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
mww
, mww_eval
,mww_wav
,mww_wav_eval
,mww_wav_cov_eval
Examples
### Simulation of ARFIMA(0,d,0)
rho <- 0.4
cov <- matrix(c(1,rho,rho,1),2,2)
d <- c(0.4,0.2)
J <- 9
N <- 2^J
resp <- fivarma(N, d, cov_matrix=cov)
x <- resp$x
long_run_cov <- resp$long_run_cov
## wavelet coefficients definition
res_filter <- scaling_filter('Daubechies',8);
filter <- res_filter$h
M <- res_filter$M
alpha <- res_filter$alpha
LU <- c(2,11)
res_mww <- mww_cov_eval(d,x,filter,LU)