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Efficient GMM Estimation with Incomplete Data

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  • Chris Muris

    (University of Bristol)

Abstract

In the standard missing data model, data are either complete or completely missing. However, applied researchers face situations with an arbitrary number of strata of incompleteness. Examples include unbalanced panels and instrumental variables settings where some observations are missing some instruments. I propose a model for settings where observations may be incomplete, with an arbitrary number of strata of incompleteness. I derive a set of moment conditions that generalizes those in Graham's (2011) standard missing data setup. I derive the associated efficiency bound and propose efficient estimators. Identification can be achieved even if it fails in each stratum of incompleteness.

Suggested Citation

  • Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
    • Handle: RePEc:tpr:restat:v:102:y:2020:i:3:p:518-530
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    Cited by:

    1. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    2. Yimin Yang & Fei Jia & Haoran Li, 2023. "Estimation of Panel Data Models with Mixed Sampling Frequencies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 514-544, June.
    3. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Abrevaya, Jason, 2019. "Missing dependent variables in fixed-effects models," Journal of Econometrics, Elsevier, vol. 211(1), pages 151-165.

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