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Least absolute deviation estimation for general fractionally integrated autoregressive moving average time series models

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  • Wu, Rongning

Abstract

We introduce a new class of ARFIMA models, which removes the restrictions that the roots of AR and MA polynomials are outside the unit circle. We establish consistency and asymptotic normality of the least absolute deviation estimator under non-Gaussian setting.

Suggested Citation

  • Wu, Rongning, 2014. "Least absolute deviation estimation for general fractionally integrated autoregressive moving average time series models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 69-76.
    • Handle: RePEc:eee:stapro:v:94:y:2014:i:c:p:69-76
    DOI: 10.1016/j.spl.2014.07.008
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    1. Rongning Wu & Richard A. Davis, 2010. "Least absolute deviation estimation for general autoregressive moving average time‐series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 98-112, March.
    2. Davis, Richard A. & Knight, Keith & Liu, Jian, 1992. "M-estimation for autoregressions with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 145-180, February.
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    Cited by:

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    2. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    3. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.

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