Structural measurement errors in nonseparable models

Stefan Hoderlein and Joachim Winter ()

Munich Reprints in Economics from University of Munich, Department of Economics

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.

Date: 2010
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Citations: View citations in EconPapers (20)

Published in Journal of Econometrics 2 157(2010): pp. 432-440

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Related works:
Journal Article: Structural measurement errors in nonseparable models (2010)
Working Paper: Structural Measurement Errors in Nonseparable Models (2009)
Working Paper: Structural Measurement Errors in Nonseparable Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:lmu:muenar:19445

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