Systematically misclassified binary dependent variables

Vidhura Tennekoon and Robert Rosenman

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 9, 2538-2555

Abstract: When a binary dependent variable is misclassified, that is, recorded in the category other than where it really belongs, probit and logit estimates are biased and inconsistent. In some cases, the probability of misclassification may vary systematically with covariates, and thus be endogenous. In this paper, we develop an estimation approach that corrects for endogenous misclassification, validate our approach using a simulation study, and apply it to the analysis of a treatment program designed to improve family dynamics. Our results show that endogenous misclassification could lead to potentially incorrect conclusions unless corrected using an appropriate technique.

Date: 2016
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Citations: View citations in EconPapers (3)

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Working Paper: Systematically Misclassified Binary Dependant Variables (2011)
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DOI: 10.1080/03610926.2014.887105

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