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Missing dependent variables in fixed-effects models

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  • Abrevaya, Jason

Abstract

This paper considers estimation of linear fixed-effects models in which the dependent variable may be missing. For cross-sectional units with dependent variables missing, use of covariate information from all time periods can provide efficiency gains relative to complete-data methods. A classical minimum distance (CMD) estimator based upon Chamberlain (1982, 1984), which is consistent under a missing-at-random (MAR) type assumption, is proposed for the static fixed-effects model. In certain circumstances, it is shown that “within” variation in the dependent variable is not even required for identification of the model parameters. The CMD estimation approach is extended to the case of (autoregressive) fixed-effects models with lagged dependent variables. Monte Carlo simulations investigate the performance of the CMD approach relative to existing methods. Extensions to models with sequential exogeneity and missing covariates are also discussed.

Suggested Citation

  • Abrevaya, Jason, 2019. "Missing dependent variables in fixed-effects models," Journal of Econometrics, Elsevier, vol. 211(1), pages 151-165.
    • Handle: RePEc:eee:econom:v:211:y:2019:i:1:p:151-165
    DOI: 10.1016/j.jeconom.2018.12.011
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    1. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
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    7. 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.
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    Cited by:

    1. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.

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