Bayesian model selection in ARFIMA models

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Bayesian model selection in ARFIMA models. / Eǧrïoǧlu, Erol; Günay, Süleyman.
In: Expert Systems with Applications, Vol. 37, No. 12, 01.01.2010, p. 8359-8364.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Eǧrïoǧlu, E & Günay, S 2010, 'Bayesian model selection in ARFIMA models', Expert Systems with Applications, vol. 37, no. 12, pp. 8359-8364. https://doi.org/10.1016/j.eswa.2010.05.047

APA

Eǧrïoǧlu, E., & Günay, S. (2010). Bayesian model selection in ARFIMA models. Expert Systems with Applications, 37(12), 8359-8364. https://doi.org/10.1016/j.eswa.2010.05.047

Vancouver

Eǧrïoǧlu E, Günay S. Bayesian model selection in ARFIMA models. Expert Systems with Applications. 2010 Jan 1;37(12):8359-8364. doi: 10.1016/j.eswa.2010.05.047

Author

Eǧrïoǧlu, Erol ; Günay, Süleyman. / Bayesian model selection in ARFIMA models. In: Expert Systems with Applications. 2010 ; Vol. 37, No. 12. pp. 8359-8364.

Bibtex

@article{a60a1508aba04297aa88ef2f4f183b62,

title = "Bayesian model selection in ARFIMA models",

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.",

keywords = "Autoregressive fractional integrated moving average models, Bayesian model selection, Long memory processes, Reversible jump Markov chain Monte Carlo",

author = "Erol Eǧr{\"i}oǧlu and S{\"u}leyman G{\"u}nay",

year = "2010",

month = jan,

day = "1",

doi = "10.1016/j.eswa.2010.05.047",

language = "English",

volume = "37",

pages = "8359--8364",

journal = "Expert Systems with Applications",

issn = "0957-4174",

publisher = "Elsevier Ltd",

number = "12",

}

RIS

TY - JOUR

T1 - Bayesian model selection in ARFIMA models

AU - Eǧrïoǧlu, Erol

AU - Günay, Süleyman

PY - 2010/1/1

Y1 - 2010/1/1

N2 - 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.

AB - 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.

KW - Autoregressive fractional integrated moving average models

KW - Bayesian model selection

KW - Long memory processes

KW - Reversible jump Markov chain Monte Carlo

U2 - 10.1016/j.eswa.2010.05.047

DO - 10.1016/j.eswa.2010.05.047

M3 - Journal article

AN - SCOPUS:77957838947

VL - 37

SP - 8359

EP - 8364

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 12

ER -

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