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Structural measurement errors in nonseparable models

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  • Hoderlein, Stefan
  • Winter, Joachim

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 limited recall for the identification of marginal effects. Our identification approach is entirely nonparametric, using Matzkin-type nonseparable models that nest a large class of potential structural models. We show that measurement error due to limited recall will generally exhibit nonstandard behavior, in particular be nonclassical and differential, even for left-hand side variables in linear models. We establish that information reduction by individuals is the critical issue for the severity of recall measurement error. In order to detect information reduction, we propose a nonparametric test statistic. Finally, we propose bounds to address identification problems resulting from recall errors. We illustrate our theoretical findings using real-world data on food consumption.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 157 (2010)
Issue (Month): 2 (August)
Pages: 432-440

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Handle: RePEc:eee:econom:v:157:y:2010:i:2:p:432-440

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Measurement error Nonparametric Survey design Nonseparable model Identification Zero homogeneity Demand;

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References

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  1. Das, J.W.M. & Dominitz, J. & Soest, A.H.O. van, 1998. "Comparing predictions and outcomes: theory and application to income changes," Open Access publications from Tilburg University urn:nbn:nl:ui:12-121742, Tilburg University.
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  3. Hoderlein, Stefan & Winter, Joachim, 2010. "Structural measurement errors in nonseparable models," Journal of Econometrics, Elsevier, vol. 157(2), pages 432-440, August.
  4. Michael Hurd & Susann Rohwedder, 2009. "Methodological Innovations in Collecting Spending Data: The HRS Consumption and Activities Mail Survey," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 435-459, December.
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  14. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
  15. Rosa L. Matzkin, 2007. "Nonparametric Survey Response Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1411-1427, November.
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Cited by:
  1. Thomas F. Crossley & Joachim K. Winter, 2012. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures National Bureau of Economic Research, Inc.
  2. Susanne Schennach, 2012. "Measurement error in nonlinear models- a review," CeMMAP working papers CWP41/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Hoderlein, Stefan & Winter, Joachim, 2010. "Structural measurement errors in nonseparable models," Munich Reprints in Economics 19445, University of Munich, Department of Economics.
  4. Susanne Schennach & Halbert White & Karim Chalak, 2007. "Local Indirect Least Squares and Average Marginal Effects in Nonseparable Structural Systems," Boston College Working Papers in Economics 680, Boston College Department of Economics, revised 26 Dec 2009.

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