Bayesian model selection in ARFIMA models

EĞRİOĞLU E., Guenay S.

EXPERT SYSTEMS WITH APPLICATIONS, vol.37, no.12, pp.8359-8364, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 12
  • Publication Date: 2010
  • Doi Number: 10.1016/j.eswa.2010.05.047
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.8359-8364
  • Hacettepe University Affiliated: Yes

Abstract

Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both model parameters and order. This kind of approach needs much time. However, in the literature, there are proposed methods which calculate model parameters and order at the same time such as reversible jump Markov chain Monte Carlo (RJMCMC) method, Carlin and Chib (CC) method. In this paper, we proposed two new methods that are using RJMCMC method. The proposed methods are compared with classical methods by a simulation study. We obtained that our methods outperform classical methods in most cases. (C) 2010 Elsevier Ltd. All rights reserved.