Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the multivariate wavelet Whittle estimation for the long-memory parameter vector d
and the long-run covariance matrix, using DWTexact
for the wavelet decomposition.
1 |
x |
data (matrix with time in rows and variables in columns). |
filter |
wavelet filter as obtain with |
LU |
bivariate vector (optional) containing
|
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.
d |
estimation of vector of long-memory parameters. |
cov |
estimation of long-run covariance matrix. |
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.
mww_eval
, mww_cov_eval
,mww_wav
,mww_wav_eval
,mww_wav_cov_eval
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ### 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(x,filter,LU)
|
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