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Robust and Asymptotically Efficient Estimation of Location in a Stationary Strong Mixing Gaussian Parametric Model

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Abstract

This paper considers the problem of robust estimation of location in a model with stationary strong mixing Gaussian parametric distributions. An estimator is found that is within epsilon of being asymptotically efficient at the Gaussian parametric distribution and is within epsilon of being optimally robust! For the robustness results a Huber-type minimax criterion is used, where minimaxing takes place over neighborhoods of the parametric Gaussian distributions. The neighborhood system considered includes distributions of strong mixing processes and allows for deviations from the normal univariate parametric distributions within a Hellinger metric neighborhood, as well as deviations from stationarity and from the Gaussian structure of independence.

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  • Donald W.K. Andrews, 1982. "Robust and Asymptotically Efficient Estimation of Location in a Stationary Strong Mixing Gaussian Parametric Model," Cowles Foundation Discussion Papers 659, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:659
    Note: CFP 725.
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d06/d0659.pdf
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    1. Andrews, Donald W K, 1986. "Stability Comparisons of Estimators," Econometrica, Econometric Society, vol. 54(5), pages 1207-1235, September.
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    Cited by:

    1. Kirill Evdokimov & Yuichi Kitamura & Taisuke Otsu, 2014. "Robust estimation of moment condition models with weakly dependent data," STICERD - Econometrics Paper Series 579, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. repec:cep:stiecm:/2014/579 is not listed on IDEAS

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