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Structural Measurement Errors in Nonseparable Models

Author

Listed:
  • Stefan Hoderlein

    (Boston College)

  • Joachim Winter

    (University of Munich)

Abstract
This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of recall errors for such key econometric issues as the identification of marginal effects or economic restrictions in structural models. Our identification approach is entirely nonparametric, using Matzkin-type nonseparable models that nest a large class of potential structural models. We establish that measurement errors due to poor recall are generally likely to exhibit nonstandard behavior, in particular be nonclassical and differential, and we provide means to deal with this situation. Moreover, our findings suggest that conventional wisdom about measurement errors may be misleading in many economic applications. For instance, under certain conditions left-hand side recall errors will be problematic even in the linear model, and quantiles will be less robust than means. Finally, we apply the main concepts put forward in this paper to real world data, and find evidence that underscores the importance of focusing on individual response behavior.

Suggested Citation

  • Stefan Hoderlein & Joachim Winter, 2009. "Structural Measurement Errors in Nonseparable Models," Boston College Working Papers in Economics 750, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:750
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hoderlein, Stefan & Winter, Joachim, 2010. "Structural measurement errors in nonseparable models," Journal of Econometrics, Elsevier, vol. 157(2), pages 432-440, August.
    2. Schennach, Susanne & White, Halbert & Chalak, Karim, 2012. "Local indirect least squares and average marginal effects in nonseparable structural systems," Journal of Econometrics, Elsevier, vol. 166(2), pages 282-302.
    3. Stefan Hoderlein & Bettina Siflinger & Joachim Winter, 2015. "Identification of structural models in the presence of measurement error due to rounding in survey responses," Boston College Working Papers in Economics 869, Boston College Department of Economics.
    4. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers CWP41/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Battistin, Erich & De Nadai, Michele & Krishnan, Nandini, 2020. "The Insights and Illusions of Consumption Measurements," CEPR Discussion Papers 14730, C.E.P.R. Discussion Papers.
    6. Paul Ruud & Daniel Schunk & Joachim Winter, 2014. "Uncertainty causes rounding: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 17(3), pages 391-413, September.
    7. Christoph Breunig & Stephan Martin, 2020. "Nonclassical Measurement Error in the Outcome Variable," Papers 2009.12665, arXiv.org, revised May 2021.
    8. Battistin, Erich & De Nadai, Michele & Krishnan, Nandini, 2023. "The insights and illusions of consumption measurements," Journal of Development Economics, Elsevier, vol. 161(C).
    9. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2014. "Measurement Error in Subjective Expectation and the Empirical Content of Economic Models," MEA discussion paper series 201414, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    10. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    11. Sriya Iyer & Chander Velu & Melvyn Weeks, 2014. "Divine Competition: Religious Organisations and Service Provision in India," Cambridge Working Papers in Economics 1409, Faculty of Economics, University of Cambridge.
    12. Thomas F. Crossley & Peter Levell & Stavros Poupakis, 2022. "Regression with an imputed dependent variable," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1277-1294, November.
    13. Ian B. Page & Erik Lichtenberg & Monica Saavoss, 2020. "Estimating Willingness to Pay from Count Data When Survey Responses are Rounded," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(3), pages 657-675, March.
    14. Batarce, Marco, 2024. "Estimation of discrete choice models with error in variables: An application to revealed preference data with aggregate service level variables," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).
    15. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2017. "The precision of subjective data and the explanatory power of economic models," Journal of Econometrics, Elsevier, vol. 200(2), pages 378-389.

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    More about this item

    Keywords

    Measurement Error; Nonparametric; Survey Design; Nonseparable Model; Identification; Zero Homogeneity; Demand;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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