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A Bayesian MCMC based estimation of Long memory in state space model

Li, Yushu LU (2014) 1st International Work-Conference on Time Series (ITISE) In International Work-Conference on Time Series (ITISE 2014) p.1341-1352
Abstract
To estimate the long memory series in the framework of state space model is rarely documented although the theoretical foundation was well built in late 90s, and the literatures concentrate mainly on the estimation in stationary case. This paper aims to estimate the parameters in a wide range of long memory series by applying approximate Maximum Likelihood Estimation (MLE) and Bayesian Monte Carlo Markov Chain (MCMC) methodology. We show that both methods perform quite well with the exception in the case that the series is nearly non-stationary, where pure MLE gives out seriously over biased estimation and the Bayesian MCMC estimation can avoid this problem when pre-knowledge is available.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
fractional difference, state space model, Kalman filter, Metropolis-Hastings algorithm
in
International Work-Conference on Time Series (ITISE 2014)
pages
1341 - 1352
publisher
Copicentro Granada SL
conference name
1st International Work-Conference on Time Series (ITISE)
external identifiers
  • WOS:000359136600149
ISBN
978-84-15814-97-4
language
English
LU publication?
yes
id
3b63615c-c0aa-4b1d-9d8d-0f353d4de752 (old id 7975656)
date added to LUP
2015-09-25 08:04:00
date last changed
2016-04-16 09:37:56
@misc{3b63615c-c0aa-4b1d-9d8d-0f353d4de752,
  abstract     = {To estimate the long memory series in the framework of state space model is rarely documented although the theoretical foundation was well built in late 90s, and the literatures concentrate mainly on the estimation in stationary case. This paper aims to estimate the parameters in a wide range of long memory series by applying approximate Maximum Likelihood Estimation (MLE) and Bayesian Monte Carlo Markov Chain (MCMC) methodology. We show that both methods perform quite well with the exception in the case that the series is nearly non-stationary, where pure MLE gives out seriously over biased estimation and the Bayesian MCMC estimation can avoid this problem when pre-knowledge is available.},
  author       = {Li, Yushu},
  isbn         = {978-84-15814-97-4},
  keyword      = {fractional difference,state space model,Kalman filter,Metropolis-Hastings algorithm},
  language     = {eng},
  pages        = {1341--1352},
  publisher    = {ARRAY(0x8d6a228)},
  series       = {International Work-Conference on Time Series (ITISE 2014)},
  title        = {A Bayesian MCMC based estimation of Long memory in state space model},
  year         = {2014},
}