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Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors

Author

Listed:
  • Yingying Dong

    (California State University, Irvine)

  • Arthur Lewbel

    (Boston College)

  • Thomas Tao Yang

    (Boston College)

Abstract

We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when regressors may be endogenous or mismeasured, or when errors are likely to be heteroskedastic. For example, such models arise when treatment is not randomly assigned and outcomes are binary. The estimators we compare are the two stage least squares linear probability model, maximum likelihood estimation, control function estimators, and special regressor methods. We specifically focus on models and associated estimators that are easy to implement. Also, for calculating choice probabilities and regressor marginal effects, we propose the average index function (AIF), which, unlike the average structural function (ASF), is always easy to estimate.

Suggested Citation

  • Yingying Dong & Arthur Lewbel & Thomas Tao Yang, 2012. "Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 789, Boston College Department of Economics, revised 15 May 2012.
    • Handle: RePEc:boc:bocoec:789
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    More about this item

    Keywords

    Binary choice; Binomial Response; Endogeneity; Measurement Error; Heteroskedasticity; discrete endogenous; censored; random coefficients; Identification; Latent Variable Model.;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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