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A Hausman test for non-ignorability

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  • Bücker, Michael
  • Krämer, Walter
  • Arnold, Matthias

Abstract

Using an empirical likelihood approach, we show that generalized linear models can still be consistently estimated even if dependent variables are not missing at random, and derive a Hausman test by comparing this estimator to the standard one.

Suggested Citation

  • Bücker, Michael & Krämer, Walter & Arnold, Matthias, 2012. "A Hausman test for non-ignorability," Economics Letters, Elsevier, vol. 114(1), pages 23-25.
    • Handle: RePEc:eee:ecolet:v:114:y:2012:i:1:p:23-25
    DOI: 10.1016/j.econlet.2011.08.025
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    References listed on IDEAS

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    1. Wong, Ka-fu, 1996. "Bootstrapping Hausman's exogeneity test," Economics Letters, Elsevier, vol. 53(2), pages 139-143, November.
    2. Schreiber Sven, 2008. "The Hausman Test Statistic can be Negative even Asymptotically," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(4), pages 394-405, August.
    3. 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.
    4. Qin J. & Leung D. & Shao J., 2002. "Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 193-200, March.
    5. Kramer, Walter & Sonnberger, Harald, 1986. "Computational pitfalls of the Hausman test," Journal of Economic Dynamics and Control, Elsevier, vol. 10(1-2), pages 163-165, June.
    6. Hsiao, Cheng, 1980. "Missing data and maximum likelihood estimation," Economics Letters, Elsevier, vol. 6(3), pages 249-253.
    7. Crook, Jonathan, 1999. "Who is discouraged from applying for credit?," Economics Letters, Elsevier, vol. 65(2), pages 165-172, November.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.

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    More about this item

    Keywords

    Hausman test; Missing data; Empirical likelihood; Reject inference; Credit scoring; Logistic regression;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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