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Discrete Choice Non-Response

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  • Esmeralda A. Ramalho
  • Richard J. Smith

Abstract

Missing values are endemic in the data sets available to econometricians. This paper suggests a semiparametrically efficient likelihood-based approach to deal with general non-ignorable missing data problems for discrete choice models. Our concern is when the dependent variable and/or covariates are unobserved for some sampling units. A supplementary random sample of observations on all covariates may be available. The key insight of this paper is the recognition of non-response as a modification of choice-based (CB) samples. Semiparametrically efficient generalized method of moments (GMM) estimation appropriate for CB samples is then adapted for the non-response framework considered in this paper. Simulation results for various GMM estimators proposed here are very encouraging. Copyright , Oxford University Press.

Suggested Citation

  • Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," Review of Economic Studies, Oxford University Press, vol. 80(1), pages 343-364.
  • Handle: RePEc:oup:restud:v:80:y:2013:i:1:p:343-364
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    File URL: http://hdl.handle.net/10.1093/restud/rds018
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    Cited by:

    1. Christoph Breunig & Enno Mammen & Anna Simoni, "undated". "Nonparametric Estimation in case of Endogenous Selection," SFB 649 Discussion Papers SFB649DP2015-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Christoph Breunig, 2015. "Testing Missing at Random using Instrumental Variables," SFB 649 Discussion Papers SFB649DP2015-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2017. "Nonparametric Estimation in Case of Endogenous Selection," Rationality and Competition Discussion Paper Series 58, CRC TRR 190 Rationality and Competition.
    4. Breunig, Christoph & Kummer, Michael & Ohnemus, Jörg & Viete, Steffen, 2016. "IT outsourcing and firm productivity: Eliminating bias from selective missingness in the dependent variable," ZEW Discussion Papers 16-092, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    5. Breunig, Christoph, 2017. "Testing Missing At Random Using Instrumental Variables," Rationality and Competition Discussion Paper Series 59, CRC TRR 190 Rationality and Competition.
    6. Melvin Stephens, Jr. & Takashi Unayama, 2015. "Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data," NBER Working Papers 21248, National Bureau of Economic Research, Inc.
    7. Laurent Davezies & Xavier d'Haultfoeuille, 2013. "Endogenous Attrition in Panels," Working Papers 2013-17, Center for Research in Economics and Statistics.
    8. Carro, Jesus & Machado, Matilde Pinto & Mora, Ricardo, 2014. "Transmission of preferences and beliefs about female labor market participation: direct evidence on the role of mothers," CEPR Discussion Papers 10218, C.E.P.R. Discussion Papers.
    9. Christoph Breunig, 2017. "Testing Missing at Random using Instrumental Variables," SFB 649 Discussion Papers SFB649DP2017-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. repec:eee:econom:v:202:y:2018:i:2:p:268-285 is not listed on IDEAS
    11. d'Haultfoeuille, Xavier, 2010. "A new instrumental method for dealing with endogenous selection," Journal of Econometrics, Elsevier, vol. 154(1), pages 1-15, January.

    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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