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Recent Advances in the Measurement Error Literature


  • Susanne M. Schennach

    () (Department of Economics, Brown University, Providence, Rhode Island 02912)


This article reviews recent significant progress made in developing estimation and inference methods for nonlinear models in the presence of mismeasured data that may or may not conform to the classical assumption of independent zero-mean errors. The aim is to cover a broad range of methods having differing levels of complexity and strength of the required assumptions. Simple approaches that form the elementary building blocks of more advanced approaches are discussed first. Then, special attention is devoted to methods that rely on readily available auxiliary variables (e.g., repeated measurements, indicators, or instrumental variables). Results relaxing most of the commonly invoked simplifying assumptions are presented (linear measurement structure, independent errors, zero-mean errors, availability of auxiliary information). This article also provides an overview of important connections with related fields, such as latent variable models, nonlinear panel data, factor models, and set identification, and applications of the methods to other fields traditionally unrelated to measurement error models.

Suggested Citation

  • Susanne M. Schennach, 2016. "Recent Advances in the Measurement Error Literature," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 341-377, October.
  • Handle: RePEc:anr:reveco:v:8:y:2016:p:341-377

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    Cited by:

    1. repec:eee:jetheo:v:172:y:2017:i:c:p:163-201 is not listed on IDEAS
    2. repec:eee:jhecon:v:58:y:2018:i:c:p:76-89 is not listed on IDEAS
    3. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Pablo Mitnik, 2018. "Estimating the Intergenerational Elasticity of Expected Income with Short-Run Income Measures: A Generalized Error-in-Variables Model," Working Papers 2018-045, Human Capital and Economic Opportunity Working Group.
    5. Andrea Bastianin & Paolo Castelnovo & Massimo Florio, 2017. "The Empirics of Regulatory Reforms Proxied by Categorical Variables: Recent Findings and Methodological Issues," Working Papers 2017.22, Fondazione Eni Enrico Mattei.
    6. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    7. Eric Blankmeyer, 2018. "Measurement Errors as Bad Leverage Points," Papers 1807.02814,
    8. repec:eee:econom:v:207:y:2018:i:1:p:129-161 is not listed on IDEAS
    9. repec:eee:econom:v:200:y:2017:i:2:p:154-168 is not listed on IDEAS
    10. Takahide Yanagi, 2018. "Inference on Local Average Treatment Effects for Misclassified Treatment," Papers 1804.03349,

    More about this item


    latent variable; factor; nonlinear; nonclassical; nonseparable;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other


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