Misclassification in binary choice models

DOI10.1016/j.jeconom.2017.06.012zbMath1388.62373OpenAlexW3124088331MaRDI QIDQ2399542

Bruce D. Meyer, Nikolas Mittag

Publication date: 24 August 2017

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2017.06.012

zbMATH Keywords

asymptotic biasmeasurement errorparametric modelsbinary choice modelsfood stampsprogram take-up

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05)

Related Items (7)

Robust active learning with binary responses ⋮ Measurement error models: editors' introduction ⋮ The need to conduct repeated classifications in a logistic regression model with misclassification in the dependent variable ⋮ Binary choice with misclassification and social interactions, with an application to peer effects in attitude ⋮ Repeated responses in misclassification binary regression: A Bayesian approach ⋮ An empirical total survey error decomposition using data combination ⋮ Testing of Poisson mean with under-reported counts

Cites Work

• Inverse probability weighted estimation for general missing data problems
• Identification and estimation of nonlinear models with misclassification error using instrumental variables: a general solution
• Partial identification of probability distributions with misclassified data
• Consistent estimation of limited dependent variable models despite misspecification of distribution
• Bounding mean regressions when a binary regressor is mismeasured
• Misclassification of the dependent variable in a discrete-response setting
• Regression with a binary independent variable subject to errors of observation
• Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecification of Distribution in Multinomial Discrete Choice Models
• Combining Micro and Macro Data in Microeconometric Models
• Modeling Discrete Choice With Response Error: Food Stamp Participation
• IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION
• Identification and Robustness with Contaminated and Corrupted Data
• Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence
• Measurement Error in Nonlinear Models

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