Structural measurement errors in nonseparable models

doi:10.1016/j.jeconom.2010.03.044

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Structural measurement errors in nonseparable models

2010-08-01

Stefan Hoderlein *

Joachim Winter

DOI：10.1016/j.jeconom.2010.03.044

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Abstract

En 中文

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. (C) 2010 Elsevier B.V. All rights reserved.

Keywords:

Measurement error

Nonparametric

Survey design

Nonseparable model

Identification

Zero homogeneity

Demand

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