LS.whittle.loglik.theta: Locally Stationary Whittle Log-likelihood theta

In LSTS: Locally Stationary Time Series

Description

Calculate the log-likelihood with σ known, through LS.whittle.loglik function.

Usage

LS.whittle.loglik.theta(
  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,
  sd.par = 1
)

Arguments

x	(type: numeric) parameter vector.
series	(type: numeric) univariate time series.
order	(type: numeric) vector corresponding to ARMA model entered.
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) d polinomial order, where d is the ARFIMA parameter.
include.d	(type: numeric) logical argument for ARFIMA models. If include.d=FALSE then the model is an ARMA process.
N	(type: numeric) value corresponding to the length of the window to compute periodogram. If N=NULL then the function will use N = \textrm{trunc}(n^{0.8}), see Dahlhaus (1998) where n is the length of the y vector.
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 TRUE. See periodogram.
sd.par	(type: numeric) value corresponding to known variance.

Details

This function computes LS.whittle.loglik with x as x = c(x, sd.par).

LSTS documentation built on July 29, 2021, 5:07 p.m.