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Asymptotic Analysis of Iterated 1-step Huber-skip M-estimators with Varying Cut-offs

Author

Listed:
  • Bent Nielsen

    (Nuffield College, Oxford)

  • Xiyu Jiao

    (Department of Economics, University of Oxford and Mansfield College)

Abstract

We consider outlier detection algorithms for time series regression based on iterated 1-step Huber-skip M-estimators. This paper analyses the role of varying cut-offs in such algorithms. The argument involves an asymptotic theory for a new class of weighted and marked empirical processes allowing for estimation errors of the scale and the regression coefficient.

Suggested Citation

  • Bent Nielsen & Xiyu Jiao, 2016. "Asymptotic Analysis of Iterated 1-step Huber-skip M-estimators with Varying Cut-offs," Economics Papers 2016-W08, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:1608
    as

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    File URL: https://www.nuffield.ox.ac.uk/economics/papers/2016/AMISTAT2016Aug24DP.pdf
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    References listed on IDEAS

    as
    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Cavaliere, Giuseppe & Georgiev, Iliyan, 2013. "Exploiting Infinite Variance Through Dummy Variables In Nonstationary Autoregressions," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1162-1195, December.
    3. repec:bot:quadip:118 is not listed on IDEAS
    4. Bollerslev, Tim & Russell, Jeffrey & Watson, Mark (ed.), 2010. "Volatility and Time Series Econometrics: Essays in Honor of Robert Engle," OUP Catalogue, Oxford University Press, number 9780199549498.
    5. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    6. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    Full references (including those not matched with items on IDEAS)

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