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Discrete choice non-response


  • Esmerelda A. Ramalho

    (Institute for Fiscal Studies)

  • Richard Smith

    () (Institute for Fiscal Studies and University of Cambridge)


Missing values are endemic in the data sets available to econometricians. This paper suggests a unified likelihood-based approach to deal with several nonignorable missing data problems for discrete choice models. Our concern is when either the dependent variable is unobserved or situations when both dependent variable and covariates are missing for some sampling units. These cases are also considered when a supplementary random sample of observations on all covariates is available. A unified treatment of these various sampling structures is presented using a formulation of the nonresponse problems as a modification of choice-based sampling. Extensions appropriate for nonresponse are detailed of Imbens' (1992) effcient generalized method of moments (GMM) estimator for choice-based samples. Simulation evidence reveals very promising results for the various GMM estimators proposed in this paper.

Suggested Citation

  • Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers CWP07/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:07/03

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    References listed on IDEAS

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    Cited by:

    1. Breunig, Christoph, 2017. "Testing Missing At Random Using Instrumental Variables," Rationality and Competition Discussion Paper Series 59, CRC TRR 190 Rationality and Competition.
    2. 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.
    3. Carro, Jesús M. & Machado, Matilde P. & Mora, Ricardo, 2014. "Transmission of preferences and beliefs about female labor market participation : direct evidence on the role of mothers," UC3M Working papers. Economics we1421, Universidad Carlos III de Madrid. Departamento de Economía.
    4. repec:eee:econom:v:202:y:2018:i:2:p:268-285 is not listed on IDEAS
    5. d'Haultfoeuille, Xavier, 2010. "A new instrumental method for dealing with endogenous selection," Journal of Econometrics, Elsevier, vol. 154(1), pages 1-15, January.
    6. 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.
    7. Christoph Breunig, 2015. "Testing Missing at Random using Instrumental Variables," SFB 649 Discussion Papers SFB649DP2015-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. 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.
    9. 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.
    10. Laurent Davezies & Xavier d'Haultfoeuille, 2013. "Endogenous Attrition in Panels," Working Papers 2013-17, Center for Research in Economics and Statistics.
    11. Christoph Breunig, 2017. "Testing Missing at Random using Instrumental Variables," SFB 649 Discussion Papers SFB649DP2017-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    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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