Misclassification in binary choice models

0478019 - NHU-C 2018 RIV CH eng J - Článek v odborném periodiku
Meyer, B. D. - Mittag, Nikolas
Misclassification in binary choice models.
Journal of Econometrics. Roč. 200, č. 2 (2017), s. 295-311. ISSN 0304-4076. E-ISSN 1872-6895
Grant CEP: GA ČR(CZ) GJ16-07603Y
Institucionální podpora: Progres-Q24
Klíčová slova: measurement error * binary choice models * program take-up
Obor OECD: Economic Theory
Impakt faktor: 1.632, rok: 2017

Bias from misclassification of binary dependent variables can be pronounced. We examine what can be learned from such contaminated data. First, we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables. Simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply attenuation. Second, we examine the bias in a prototypical application. Erroneously restricting the covariance of misclassification and covariates aggravates the bias for all estimators we examine. Estimators that relax this restriction perform well if a model of misclassification or validation data is available.
Trvalý link: http://hdl.handle.net/11104/0274250