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A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

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  • Brian Boyer
  • James McDonald
  • Whitney Newey

Abstract

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.

Suggested Citation

  • Brian Boyer & James McDonald & Whitney Newey, 2003. "A Comparison of Partially Adaptive and Reweighted Least Squares Estimation," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 115-134.
    • Handle: RePEc:taf:emetrv:v:22:y:2003:i:2:p:115-134
    DOI: 10.1081/ETC-120020459
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    Citations

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    Cited by:

    1. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    2. Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
    3. James B. McDonald & Richard A. Michelfelder & Panayiotis Theodossiou, 2009. "Robust Regression Estimation Methods and Intercept Bias: A Capital Asset Pricing Model Application," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 293-321, September.
    4. James Mcdonald & Richard Michelfelder & Panayiotis Theodossiou, 2010. "Robust estimation with flexible parametric distributions: estimation of utility stock betas," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 375-387.
    5. Hansen, James V. & McDonald, James B. & Turley, Robert S., 2006. "Partially adaptive robust estimation of regression models and applications," European Journal of Operational Research, Elsevier, vol. 170(1), pages 132-143, April.

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