A Hausman test for non-ignorability
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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- 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.
- 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.
- Hausman, Jerry A, 1978.
"Specification Tests in Econometrics,"
Econometric Society, vol. 46(6), pages 1251-71, November.
- 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.
- Wong, Ka-fu, 1996. "Bootstrapping Hausman's exogeneity test," Economics Letters, Elsevier, vol. 53(2), pages 139-143, November.
- Crook, Jonathan, 1999. "Who is discouraged from applying for credit?," Economics Letters, Elsevier, vol. 65(2), pages 165-172, November.
- Hsiao, Cheng, 1980. "Missing data and maximum likelihood estimation," Economics Letters, Elsevier, vol. 6(3), pages 249-253.
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