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An analysis of the indicator saturation estimator as a robust regression estimator

Author

Listed:
  • Søren Johansen

    (Department of Economics, University of Copenhagen and CREATES, University of Aarhus)

  • Bent Nielsen

    (Department of Economics, University of Oxford)

Abstract

An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber’s skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered.

Suggested Citation

  • Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression estimator," Economics Papers 2008-W03, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:0803
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    References listed on IDEAS

    as
    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    2. Nielsen, Bent, 2005. "Strong Consistency Results For Least Squares Estimators In General Vector Autoregressions With Deterministic Terms," Econometric Theory, Cambridge University Press, vol. 21(3), pages 534-561, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    2. Neil R. Ericsson, 2008. "The Fragility of Sensitivity Analysis: An Encompassing Perspective," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 895-914, December.
    3. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.
    4. Neil R. Ericsson & Steven B. Kamin, 2008. "Constructive data mining: modeling Argentine broad money demand," International Finance Discussion Papers 943, Board of Governors of the Federal Reserve System (U.S.).

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    Keywords

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    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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