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Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

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  • Nima Shariati
  • Hamid Shahriari
  • Rasoul Shafaei

Abstract

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast− τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.

Suggested Citation

  • Nima Shariati & Hamid Shahriari & Rasoul Shafaei, 2014. "Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(21), pages 4445-4470, November.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:21:p:4445-4470
    DOI: 10.1080/03610926.2012.724504
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