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A Hausman-type test to detect the presence of influential outliers in regression analysis

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  • Dehon, Catherine
  • Gassner, Marjorie
  • Verardi, Vincenzo

Abstract

In regression analysis, classical estimations may be excessively influenced by a few atypical observations. We propose a Hausman-type test to balance robustness and efficiency and to check whether a robust method should be implemented. An economic application is presented.

Suggested Citation

  • Dehon, Catherine & Gassner, Marjorie & Verardi, Vincenzo, 2009. "A Hausman-type test to detect the presence of influential outliers in regression analysis," Economics Letters, Elsevier, vol. 105(1), pages 64-67, October.
    • Handle: RePEc:eee:ecolet:v:105:y:2009:i:1:p:64-67
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    References listed on IDEAS

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    1. Jan- Sturm & Jakob de Haan, 2005. "Determinants of long-term growth: New results applying robust estimation and extreme bounds analysis," Empirical Economics, Springer, vol. 30(3), pages 597-617, October.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Christophe Croux & Stefan Aelst & Catherine Dehon, 2003. "Bounded influence regression using high breakdown scatter matrices," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 265-285, June.
    4. Jakob de Haan & Jan-Egbert Sturm, 2005. "Determinants of Long-term Growth: New Results Applying Roboust Estimation and Extreme Bounds," TWI Research Paper Series 12, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    5. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. ‘Metrics Monday: Outliers
      by Marc F. Bellemare in Marc F. Bellemare on 2015-07-20 14:00:00

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    Cited by:

    1. Kaffine, Daniel T. & Davis, Graham A., 2017. "A multi-row deletion diagnostic for influential observations in small-sample regressions," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 133-145.
    2. Paola Montero Ledezma, 2017. "The Growth of World Trade: The Robustness of the Evidence," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 2(1), pages 5-19.

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