Identification and Estimation of Heteroskedastic Binary Choice Models with Endogenous Dummy Regressors

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  • This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as https://doi.org/10.1111/ectj.12109

Summary

In this paper, we consider the semiparametric identification and estimation of a heteroskedastic binary choice model with endogenous dummy regressors and no parametric restriction on error term distribution. Our approach addresses various drawbacks associated with previous estimators proposed for this model. It allows for (i) general multiplicative heteroskedasticity in both selection and outcome equations, (ii) a nonparametric selection mechanism, and (iii) multiple discrete endogenous regressors. The resulting three-stage estimator is shown to be asymptotically normal, with a convergence rate that can be arbitrarily close to n−1/2 if certain smoothness assumptions are satisfied. Simulation results show that our estimator performs reasonably well infinite samples. Our approach is then used to study the intergenerational transmission of smoking habits in British households.

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