The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters...

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Main Authors: Erol Egrioglu, Cagdas Hakan Aladag, Cem Kadilar
Format: Article
Language:English
Published: Wiley 2011-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2011/691058
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author Erol Egrioglu
Cagdas Hakan Aladag
Cem Kadilar
author_facet Erol Egrioglu
Cagdas Hakan Aladag
Cem Kadilar
author_sort Erol Egrioglu
collection DOAJ
description Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.
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institution Kabale University
issn 1687-952X
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language English
publishDate 2011-01-01
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series Journal of Probability and Statistics
spelling doaj-art-8af49376c2c64b2c9ec434a123f56f702025-02-03T01:29:59ZengWileyJournal of Probability and Statistics1687-952X1687-95382011-01-01201110.1155/2011/691058691058The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA ModelsErol Egrioglu0Cagdas Hakan Aladag1Cem Kadilar2Department of Statistics, Ondokuz Mayis University, 55139 Samsun, TurkeyDepartment of Statistics, Hacettepe University, Beytepe, 06800 Ankara, TurkeyDepartment of Statistics, Hacettepe University, Beytepe, 06800 Ankara, TurkeySeasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.http://dx.doi.org/10.1155/2011/691058
spellingShingle Erol Egrioglu
Cagdas Hakan Aladag
Cem Kadilar
The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
Journal of Probability and Statistics
title The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
title_full The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
title_fullStr The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
title_full_unstemmed The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
title_short The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models
title_sort css and the two staged methods for parameter estimation in sarfima models
url http://dx.doi.org/10.1155/2011/691058
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