An Efficient Semiparametric Estimator for Binary Response Models
提出一种无需假设选择概率函数具体形式的离散选择模型估计量，该估计量一致、渐近正态且达到半参数效率界，蒙特卡洛证据表明其小样本表现良好。
This paper proposes an estimator for discrete choice models that makes no assumption concerning the functional form of the choice probability function, where this function can be characterized by an index. The estimator is shown to be consistent, asymptotically normally distributed, and to achieve the semiparametric efficiency bound. Monte-Carlo evidence indicates that there may be only modest efficiency losses relative to maximum likelihood estimation when the distribution of the disturbances is known, and that the small-sample behavior of the estimator in other cases is good.