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A Hausman test for non-ignorability

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

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.

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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 114 (2012)
Issue (Month): 1 ()
Pages: 23-25

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Handle: RePEc:eee:ecolet:v:114:y:2012:i:1:p:23-25
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  1. Crook, Jonathan, 1999. "Who is discouraged from applying for credit?," Economics Letters, Elsevier, vol. 65(2), pages 165-172, November.
  2. Hsiao, Cheng, 1980. "Missing data and maximum likelihood estimation," Economics Letters, Elsevier, vol. 6(3), pages 249-253.
  3. 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.
  4. 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.
  5. Hausman, Jerry A, 1978. "Specification Tests in Econometrics," Econometrica, Econometric Society, vol. 46(6), pages 1251-71, November.
  6. Wong, Ka-fu, 1996. "Bootstrapping Hausman's exogeneity test," Economics Letters, Elsevier, vol. 53(2), pages 139-143, November.
  7. Sven Schreiber, 2008. "The Hausman Test Statistic can be Negative even Asymptotically," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(4), pages 394-405, August.
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