TWO NEW METHODS FOR BAYESIAN MODEL SELECTION IN ARFIMA MODELS

by Erol E?R?O?LU, Süleyman GÜNAY .

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.

Key Words: Bayesian Model Selection, Reversible Jump Markov Chain Monte Carlo, Autoregressive Fractional Integrated Moving Average Models, Long memory processes

Authors:
Erol E?rio?lu, erole@omu.edu.tr
Süleyman Günay, sgunay@hacettepe.edu.tr

Editor: Peiris Shelton, shelton@maths.usyd.edu.au

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