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Relaxing Conditional Independence in an Endogenous Binary Response Model

Author

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  • Alyssa Carlson

    (Department of Economics, University of Missouri-Columbia)

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.

Suggested Citation

  • Alyssa Carlson, 2021. "Relaxing Conditional Independence in an Endogenous Binary Response Model," Working Papers 2113, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:2113
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    References listed on IDEAS

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    More about this item

    Keywords

    Binary choice model; Endogenous regressors; Control function; Heteroskedasticity;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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