Relaxing conditional independence in an endogenous binary response model

Alyssa Carlson

Journal of Econometrics, 2023, vol. 232, issue 2, 490-500

Abstract: For binary response models, the literature primarily addresses endogeneity by a control function approach assuming conditional independence (CF-CI). However, as the literature also notes, CF-CI implies conditions like homoskedasticity (of the latent error with respect to the instruments) that fail in many empirical settings. I propose an alternative approach that allows for heteroskedasticity, achieving identification with a conditional mean restriction. These identification results apply to a latent Gaussian error term with flexibly parametrized heteroskedasticity. I propose a two step conditional maximum likelihood estimator and derive its asymptotic distribution. In simulations, the new estimator outperforms others when CF-CI fails and is fairly robust to distributional misspecification.

Keywords: Binary choice model; Control function; Heteroskedasticity; Average structural function (search for similar items in EconPapers)
Date: 2023
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Related works:
Working Paper: Relaxing Conditional Independence in an Endogenous Binary Response Model (2021)
Working Paper: Relaxing Conditional Independence in an Endogenous Binary Response Model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:2:p:490-500

DOI: 10.1016/j.jeconom.2021.09.015

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