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Misclassification in Binary Choice Models with Sample Selection

Department of Methods and Models for Economics, Territory and Finance - Sapienza University of Rome Via del Castro Laurenziano 9, 00161 Rome, Italy
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These authors contributed equally to this work.
Econometrics 2019, 7(3), 32; https://doi.org/10.3390/econometrics7030032
Received: 7 January 2019 / Revised: 5 July 2019 / Accepted: 17 July 2019 / Published: 24 July 2019
Most empirical work in the social sciences is based on observational data that are often both incomplete, and therefore unrepresentative of the population of interest, and affected by measurement errors. These problems are very well known in the literature and ad hoc procedures for parametric modeling have been proposed and developed for some time, in order to correct estimate’s bias and obtain consistent estimators. However, to our best knowledge, the aforementioned problems have not yet been jointly considered. We try to overcome this by proposing a parametric approach for the estimation of the probabilities of misclassification of a binary response variable by incorporating them in the likelihood of a binary choice model with sample selection. View Full-Text
Keywords: misclassified dependent variable; sample selection bias; undeclared work misclassified dependent variable; sample selection bias; undeclared work
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Arezzo, M.F.; Guagnano, G. Misclassification in Binary Choice Models with Sample Selection. Econometrics 2019, 7, 32.

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