Bayesian model selection in ARFIMA models

Eǧrïoǧlu, Erol and Günay, Süleyman (2010) Bayesian model selection in ARFIMA models. Expert Systems with Applications, 37 (12). pp. 8359-8364. ISSN 0957-4174

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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.

Item Type:
Journal Article
Journal or Publication Title:
Expert Systems with Applications
Uncontrolled Keywords:
?? autoregressive fractional integrated moving average modelsbayesian model selectionlong memory processesreversible jump markov chain monte carlogeneral engineeringcomputer science applicationsartificial intelligenceengineering(all) ??
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Deposited On:
13 Dec 2019 15:55
Last Modified:
16 Jul 2024 11:20