LS.kalman: Kalman filter for locally stationary processes

In LSTS: Locally Stationary Time Series

Description

This function run the state-space equations for expansion infinite of moving average in processes LS-ARMA or LS-ARFIMA.

Usage

LS.kalman(
  series,
  start,
  order = c(p = 0, q = 0),
  ar.order = NULL,
  ma.order = NULL,
  sd.order = NULL,
  d.order = NULL,
  include.d = FALSE,
  m = NULL
)

Arguments

series	(type: numeric) univariate time series.
start	(type: numeric) numeric vector, initial values for parameters to run the model.
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.
m	(type: numeric) truncation order of the MA infinity process. By default m = 0.25n^{0.8} where n the length of series.

Details

The model fit is done using the Whittle likelihood, while the generation of innovations is through Kalman Filter. Details about ar.order, ma.order, sd.order and d.order can be viewed in LS.whittle.

Value

A list with:

residuals	standard residuals.
fitted_values	model fitted values.
delta	variance prediction error.

References

For more information on theoretical foundations and estimation methods see \insertRefbrockwell2002introductionLSTS \insertRefpalma2007longLSTS \insertRefpalma2013estimationLSTS

Examples

fit_kalman <- LS.kalman(malleco, start(malleco))

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