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Efficient estimation with missing data and endogeneity

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  • Bhavna Rai

Abstract

I study the problem of missing values in the outcome and endogenous covariates in linear models. I propose an estimator that improves efficiency relative to a complete cases 2SLS. Unlike traditional imputation, my estimator is consistent even if the model contains nonlinear functions – like squares and interactions – of the endogenous covariates. It can also be used to combine data sets with missing outcome, missing endogenous covariates, and no missing variables. It includes the well-known “Two-Sample 2SLS” as a special case under weaker assumptions than the corresponding literature.

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  • Bhavna Rai, 2023. "Efficient estimation with missing data and endogeneity," Econometric Reviews, Taylor & Francis Journals, vol. 42(2), pages 220-239, February.
    • Handle: RePEc:taf:emetrv:v:42:y:2023:i:2:p:220-239
    DOI: 10.1080/07474938.2023.2178089
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