Misclassification in Binary Choice Models with Sample Selection
Maria Felice Arezzo () and
Giuseppina Guagnano ()
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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
Giuseppina Guagnano: Department of Methods and Models for Economics, Territory and Finance - Sapienza University of Rome Via del Castro Laurenziano 9, 00161 Rome, Italy
Econometrics, 2019, vol. 7, issue 3, 1-19
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.
Keywords: misclassified dependent variable; sample selection bias; undeclared work (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:7:y:2019:i:3:p:32-:d:251289
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