Binary Response Models: Logits, Probits and Semiparametrics
介绍了Logit和Probit二元响应模型的估计方法，讨论了线性概率模型的缺陷，并描述了避免函数形式假设的半参数和非参数模型。
A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.