Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors
AbstractWe 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 heteroscedastic. 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.
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Bibliographic InfoArticle provided by Canadian Economics Association in its journal Canadian Journal of Economics.
Volume (Year): 45 (2012)
Issue (Month): 3 (August)
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Postal: Canadian Economics Association Prof. Steven Ambler, Secretary-Treasurer c/o Olivier Lebert, CEA/CJE/CPP Office C.P. 35006, 1221 Fleury Est Montréal, Québec, Canada H2C 3K4
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Find related papers by JEL classification:
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
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