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M-estimation for general ARMA Processes with Infinite Variance

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  • RONGNING WU

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  • Rongning Wu, 2013. "M-estimation for general ARMA Processes with Infinite Variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 571-591, September.
    • Handle: RePEc:bla:scjsta:v:40:y:2013:i:3:p:571-591
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    References listed on IDEAS

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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. Jian Huang & Yudi Pawitan, 2000. "Quasi‐likelihood Estimation of Non‐invertible Moving Average Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 689-702, December.
    3. J.‐W. Lin & A. I. McLeod, 2008. "Portmanteau tests for ARMA models with infinite variance," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 600-617, May.
    4. Andrews, Beth & Davis, Richard A. & Jay Breidt, F., 2006. "Maximum likelihood estimation for all-pass time series models," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1638-1659, August.
    5. 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.
    6. Lii, Keh-Shin & Rosenblatt, Murray, 1992. "An approximate maximum likelihood estimation for non-Gaussian non-minimum phase moving average processes," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 272-299, November.
    7. Davis, Richard A., 1996. "Gauss-Newton and M-estimation for ARMA processes with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 63(1), pages 75-95, October.
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