Mayer, A. and Wied, D. (2026). Endogeneity Corrections in Binary Outcome Models With Nonlinear Transformations: Identification and Inference Oxford Bulletin of Economics and Statistics, 88(1):45--56.
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Affiliated author
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Publication year2026
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JournalOxford Bulletin of Economics and Statistics
For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: Either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application to German insolvency data illustrate the usefulness of the method.