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Asymptotic theory for iterated one-step Huber-skip estimators

Author

Listed:
  • Søren Johansen

    () (University of Copenhagen and CREATES)

  • Bent Nielsen

    () (Department of Economics, University of Oxford)

Abstract

Iterated one-step Huber-skip M-estimators are considered for regression problems. Each one-step estimator is a reweighted least squares estimators with zero/one weights determined by the initial estimator and the data. The asymptotic theory is given for iteration of such estimators using a tightness argument. The results apply to stationary as well as non-stationary regression problems.

Suggested Citation

  • Søren Johansen & Bent Nielsen, 2011. "Asymptotic theory for iterated one-step Huber-skip estimators," CREATES Research Papers 2011-40, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2011-40
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    File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_40.pdf
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    References listed on IDEAS

    as
    1. Søren Johansen & Bent Nielsen, 2010. "Discussion of The Forward Search: Theory and Data Analysis by Anthony C. Atkinson, Marco Riani, and Andrea Ceroli," Discussion Papers 10-06, University of Copenhagen. Department of Economics.
    2. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    3. repec:bot:quadip:118 is not listed on IDEAS
    4. Castle, Jennifer & Shephard, Neil (ed.), 2009. "The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry," OUP Catalogue, Oxford University Press, number 9780199237197.
    5. Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression," Discussion Papers 08-03, University of Copenhagen. Department of Economics.
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    More about this item

    Keywords

    Huber-skip; iteration; one-step M-estimators; unit roots.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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