View source: R/ls_whittle_loglik.R
LS.whittle.loglik | R Documentation |
This function computes Whittle estimator for LS-ARMA and LS-ARFIMA models, in data with mean zero. If mean is not zero, then it is subtracted to data.
LS.whittle.loglik(
x,
series,
order = c(p = 0, q = 0),
ar.order = NULL,
ma.order = NULL,
sd.order = NULL,
d.order = NULL,
include.d = FALSE,
N = NULL,
S = NULL,
include.taper = TRUE
)
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 time-varying parameters can be carried out by means of the Whittle log-likelihood function proposed by Dahlhaus (1997),
L_n(\theta) = \frac{1}{4\pi}\frac{1}{M} \int_{-\pi}^{\pi}
\bigg\{log f_{\theta}(u_j,\lambda) +
\frac{I_N(u_j, \lambda)}{f_{\theta}(u_j,\lambda)}\bigg\}\,d\lambda
where M
is the number of blocks, N
the length of the series per
block, n =S(M-1)+N
, S
is the shift from block to block,
u_j =t_j/n
, t_j =S(j-1)+N/2
, j =1,\ldots,M
and
\lambda
the Fourier frequencies in the block
(2\,\pi\,k/N
, k = 1,\ldots, N
).
For more information on theoretical foundations and estimation methods see \insertRefbrockwell2002introductionLSTS \insertRefpalma2010efficientLSTS
nlminb
, LS.kalman
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