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Covariate measurement and endogeneity

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  • Millimet, Daniel L.

Abstract

The effects of improving covariate measurement are investigated when the covariate is endogenous even in the absence of measurement error. Reducing measurement error can exacerbate the finite sample bias of Two-Stage Least Squares. An application reveals this is of practical importance.

Suggested Citation

  • Millimet, Daniel L., 2015. "Covariate measurement and endogeneity," Economics Letters, Elsevier, vol. 136(C), pages 59-63.
    • Handle: RePEc:eee:ecolet:v:136:y:2015:i:c:p:59-63
    DOI: 10.1016/j.econlet.2015.08.023
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    Cited by:

    1. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    2. Adam I. Biener & Chad Meyerhoefer & John Cawley, 2024. "Non‐classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity," Health Economics, John Wiley & Sons, Ltd., vol. 33(11), pages 2558-2574, November.
    3. Dong, Hao & Millimet, Daniel L., 2023. "Embrace the Noise: It Is OK to Ignore Measurement Error in a Covariate, Sometimes," IZA Discussion Papers 16508, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Measurement error; Endogeneity; Two-Stage Least Squares;
    All these keywords.

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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