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When outcome heterogeneously matters for selection: a generalized selection correction estimator

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  • Arndt Reichert
  • Harald Tauchmann

Abstract

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

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  • Arndt Reichert & Harald Tauchmann, 2014. "When outcome heterogeneously matters for selection: a generalized selection correction estimator," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 762-768, March.
    • Handle: RePEc:taf:applec:v:46:y:2014:i:7:p:762-768
    DOI: 10.1080/00036846.2013.851780
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    1. 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.
    2. 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, July.
    3. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    4. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    5. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    6. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2012. "Does Money Burn Fat? Evidence from a Randomized Experiment," IZA Discussion Papers 6888, Institute of Labor Economics (IZA).
    7. 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.
    8. 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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    Cited by:

    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. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2012. "Does Money Burn Fat? Evidence from a Randomized Experiment," IZA Discussion Papers 6888, Institute of Labor Economics (IZA).
    3. repec:zbw:rwirep:0368 is not listed on IDEAS

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    More about this item

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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