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

Author

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  • Erol Egrioglu
  • Cagdas Hakan Aladag
  • Cem Kadilar

Abstract

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.

Suggested Citation

  • Erol Egrioglu & Cagdas Hakan Aladag & Cem Kadilar, 2011. "The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models," Journal of Probability and Statistics, Hindawi, vol. 2011, pages 1-11, August.
    • Handle: RePEc:hin:jnljps:691058
    DOI: 10.1155/2011/691058
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    Cited by:

    1. Bastos, Guadalupe & García-Martos, Carolina, 2017. "BIAS correction for dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24029, Universidad Carlos III de Madrid. Departamento de Estadística.

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