Misclassification in Binary Choice Models

65 Pages Posted: 24 May 2013

See all articles by Bruce D. Meyer

Bruce D. Meyer

University of Chicago - Harris School of Public Policy; National Bureau of Economic Research (NBER)

Nikolas Mittag

University of Chicago - Harris School of Public Policy

Multiple version iconThere are 2 versions of this paper

Date Written: May 01, 2013

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.

Suggested Citation

Meyer, Bruce D. and Mittag, Nikolas, Misclassification in Binary Choice Models (May 01, 2013). US Census Bureau Center for Economic Studies Paper No. CES-WP- 13-27, Available at SSRN: https://ssrn.com/abstract=2269315 or http://dx.doi.org/10.2139/ssrn.2269315

Bruce D. Meyer (Contact Author)

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
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(773) 702-2712 (Phone)

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
United States

Nikolas Mittag

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

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