AN EFFICIENT SEMIPARAMETRIC ESTIMATOR FOR BINARY RESPONSE MODELS
AN EFFICIENT SEMIPARAMETRIC ESTIMATOR FOR BINARY RESPONSE MODELS
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DOI：
10.2307/2951556
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发表时间：
1993-03-01
期刊：
影响因子：
6.1
通讯作者：
SPADY, RH
中科院分区：
文献类型：
--
作者：
KLEIN, RW;SPADY, RH
本文提出了一种离散选择模型的估计量，它不对选择概率函数的函数形式作任何假设，其中该函数可以用一个指数来表征。估计是一致的，渐近正态分布，并实现半参数效率界。蒙特-卡罗证据表明，可能只有适度的效率损失相对于最大似然估计时，分布的干扰是已知的，估计量的小样本行为在其他情况下是好的。
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.