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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
  • Yushu Li
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Economics

Keywords

  • fractional difference, state space model, Kalman filter, Metropolis-Hastings algorithm
Original languageEnglish
Title of host publicationInternational Work-Conference on Time Series (ITISE 2014)
PublisherCopicentro Granada SL
Pages1341-1352
ISBN (Print)9788415814974
Publication statusPublished - 2014
Publication categoryResearch
Peer-reviewedYes
Event1st International Work-Conference on Time Series (ITISE) - Granada, Spain
Duration: 2014 Jun 252014 Jun 27

Conference

Conference1st International Work-Conference on Time Series (ITISE)
CountrySpain
CityGranada
Period2014/06/252014/06/27