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MISCLASSIFICATION IN BINARY CHOICE MODELS

Bruce Meyer and Nikolas Mittag ()

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: We derive the asymptotic bias from misclassification of the dependent variable in binary choice models. Measurement error is necessarily non-classical in this case, which leads to bias in linear and non-linear models even if only the dependent variable is mismeasured. A Monte Carlo study and an application to food stamp receipt show that the bias formulas are useful to analyze the sensitivity of substantive conclusions, to interpret biased coefficients and imply features of the estimates that are robust to misclassification. Using administrative records linked to survey data as validation data, we examine estimators that are consistent under misclassification. They can improve estimates if their assumptions hold, but can aggravate the problem if the assumptions are invalid. The estimators differ in their robustness to such violations, which can be improved by incorporating additional information. We propose tests for the presence and nature of misclassification that can help to choose an estimator.

Keywords: measurement error; binary choice models; program take-up; food stamps. (search for similar items in EconPapers)
JEL-codes: C18 C81 D31 I38 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2013-05
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Citations: View citations in EconPapers (10) Track citations by RSS feed

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https://www2.census.gov/ces/wp/2013/CES-WP-13-27.pdf First version, 2013 (application/pdf)

Related works:
Working Paper: Misclassification in Binary Choice Models (2014) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:13-27

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