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Misclassification in Binary Choice Models with Sample Selection

Author

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  • Maria Felice Arezzo

    (Department of Methods and Models for Economics, Territory and Finance - Sapienza University of Rome Via del Castro Laurenziano 9, 00161 Rome, Italy
    These authors contributed equally to this work.)

  • Giuseppina Guagnano

    (Department of Methods and Models for Economics, Territory and Finance - Sapienza University of Rome Via del Castro Laurenziano 9, 00161 Rome, Italy
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Maria Felice Arezzo & Giuseppina Guagnano, 2019. "Misclassification in Binary Choice Models with Sample Selection," Econometrics, MDPI, vol. 7(3), pages 1-19, July.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:3:p:32-:d:251289
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    References listed on IDEAS

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    1. Jorge González Chapela, 2022. "A Binary Choice Model with Sample Selection and Covariate-Related Misclassification," Econometrics, MDPI, vol. 10(2), pages 1-20, March.
    2. Jorge González Chapela, 2022. "Is there a patience premium on migration?," Empirical Economics, Springer, vol. 63(4), pages 2025-2055, October.

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