Description Usage Arguments Details References See Also
View source: R/ls_whittle_loglik.R
This function computes Whittle estimator for LSARMA and LSARFIMA models, in data with mean zero. If mean is not zero, then it is subtracted to data.
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x 
(type: numeric) parameter vector. 
series 
(type: numeric) univariate time series. 
order 
(type: numeric) vector corresponding to 
ar.order 
(type: numeric) AR polimonial order. 
ma.order 
(type: numeric) MA polimonial order. 
sd.order 
(type: numeric) polinomial order noise scale factor. 
d.order 
(type: numeric) 
include.d 
(type: numeric) logical argument for 
N 
(type: numeric) value corresponding to the length of the window to
compute periodogram. If 
S 
(type: numeric) value corresponding to the lag with which will go taking the blocks or windows. 
include.taper 
(type: logical) logical argument that by default is

The estimation of the timevarying parameters can be carried out by means of the Whittle loglikelihood function proposed by Dahlhaus (1997),
L_n(θ) = \frac{1}{4π}\frac{1}{M} \int_{π}^{π} \bigg\{log f_{θ}(u_j,λ) + \frac{I_N(u_j, λ)}{f_{θ}(u_j,λ)}\bigg\}\,dλ
where M is the number of blocks, N the length of the series per block, n =S(M1)+N, S is the shift from block to block, u_j =t_j/n, t_j =S(j1)+N/2, j =1,…,M and λ the Fourier frequencies in the block (2\,π\,k/N, k = 1,…, N).
For more information on theoretical foundations and estimation methods see \insertRefbrockwell2002introductionLSTS \insertRefpalma2010efficientLSTS
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