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Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference

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  • Alexander Mayer
  • Dominik Wied

Abstract

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 on German insolvency data illustrate the usefulness of the method.

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  • Alexander Mayer & Dominik Wied, 2024. "Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference," Papers 2408.06977, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2408.06977
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    References listed on IDEAS

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    1. Roger Klein & Francis Vella, 2009. "A semiparametric model for binary response and continuous outcomes under index heteroscedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 735-762.
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    6. Jushan Bai & Serena Ng, 2008. "Extremum Estimation when the Predictors are Estimated from Large Panels," Annals of Economics and Finance, Society for AEF, vol. 9(2), pages 201-222, November.
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