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When Outcome Heterogeneously matters for Selection: a Generalized Selection Correction Estimator

  • Tauchmann, Harald
  • Reichert, Arndt

The classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inconsistent if an interaction of the outcome variable and an explanatory variable matters for selection. To address this specification problem, a full information maximum likelihood estimator and a simple two-step estimator are developed. Monte-Carlo simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and the ordinary heckit procedure, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for weight loss among the obese. Estimation results indicate that the choice of the estimation procedure clearly matters.

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File URL: http://econstor.eu/bitstream/10419/79698/1/VfS_2013_pid_99.pdf
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Paper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order with number 79698.

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Date of creation: 2013
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Handle: RePEc:zbw:vfsc13:79698
Contact details of provider: Web page: http://www.socialpolitik.org/
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  1. Boris Augurzky & Thomas K. Bauer & Arndt R. Reichert & Christoph M. Schmidt & Harald Tauchmann, 2012. "Does Money Burn Fat? – Evidence from a Randomized Experiment," Ruhr Economic Papers 0368, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  2. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
  3. Astrid Grasdal, 2001. "The performance of sample selection estimators to control for attrition bias," Health Economics, John Wiley & Sons, Ltd., vol. 10(5), pages 385-398.
  4. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
  5. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
  6. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-65, May.
  7. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
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