Relaxing Conditional Independence in an Endogenous Binary Response Model

Alyssa Carlson

No 2113, Working Papers from Department of Economics, University of Missouri

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 exibly 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; Endogenous regressors; Control function; Heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C31 C35 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2021-09
New Economics Papers: this item is included in nep-dcm, nep-isf and nep-ore
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Related works:
Journal Article: Relaxing conditional independence in an endogenous binary response model (2023)
Working Paper: Relaxing Conditional Independence in an Endogenous Binary Response Model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:umc:wpaper:2113

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