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

Yingying Dong, Arthur Lewbel and Thomas Tao Yang

No 789, Boston College Working Papers in Economics from Boston College Department of Economics

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.

Keywords: Binary choice; Binomial Response; Endogeneity; Measurement Error; Heteroskedasticity; discrete endogenous; censored; random coefficients; Identification; Latent Variable Model. (search for similar items in EconPapers)
JEL-codes: C25 C26 (search for similar items in EconPapers)
Date: 2012-02-15, Revised 2012-05-15
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (136)

Published, Canadian Journal of Economics, 2012, 45:3, 809-829

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Journal Article: Comparing features of convenient estimators for binary choice models with endogenous regressors (2012)
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