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A GMM Approach for Dealing with Missing Data on Regressors

Author

Listed:
  • Jason Abrevaya

    (University of Texas)

  • Stephen G. Donald

    (University of Texas)

Abstract

Missing data are a common challenge facing empirical researchers. This paper presents a general GMM framework and estimator for dealing with missing values of an explanatory variable in linear regression analysis. The GMM estimator is efficient under assumptions needed for consistency of linear-imputation methods. The estimator, which also allows for a specification test of the missingness assumptions, is compared to existing linear imputation, complete data, and dummy variable methods commonly used in empirical research. The dummy variable method is generally inconsistent even when data are missing completely at random, and the dummy variable method, when consistent, can be less efficient than the complete data method.

Suggested Citation

  • Jason Abrevaya & Stephen G. Donald, 2017. "A GMM Approach for Dealing with Missing Data on Regressors," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 657-662, July.
    • Handle: RePEc:tpr:restat:v:99:y:2017:i:4:p:657-662
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    Cited by:

    1. Abrevaya, Jason, 2019. "Missing dependent variables in fixed-effects models," Journal of Econometrics, Elsevier, vol. 211(1), pages 151-165.
    2. Bang, Minji & Gao, Wayne Yuan & Postlewaite, Andrew & Sieg, Holger, 2023. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 892-921.
    3. Figueiredo, Erik & Lima, Luiz Renato & Orefice, Gianluca, 2020. "Migration, trade and spillover effects," Journal of Comparative Economics, Elsevier, vol. 48(2), pages 405-421.
    4. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
    5. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
    6. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.

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