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Conditional moment models with data missing at random

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  • M. Hristache
  • V. Patilea

Abstract

SummaryWe consider a general statistical model defined by moment restrictions when data are missing at random. Using inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. Our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. The equivalence result sheds new light on various aspects of missing data, and provides guidelines for building efficient estimators.

Suggested Citation

  • M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:3:p:735-742.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx025
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    References listed on IDEAS

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    Cited by:

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    2. Guo, Xu & Fang, Yun & Zhu, Xuehu & Xu, Wangli & Zhu, Lixing, 2018. "Semiparametric double robust and efficient estimation for mean functionals with response missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 325-339.
    3. Hristache, Marian & Patilea, Valentin, 2021. "Equivalent models for observables under the assumption of missing at random," Econometrics and Statistics, Elsevier, vol. 20(C), pages 153-165.

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