SEMIPARAMETRIC LEAST-SQUARES (SLS) AND WEIGHTED SLS ESTIMATION OF SINGLE-INDEX MODELS
SEMIPARAMETRIC LEAST-SQUARES (SLS) AND WEIGHTED SLS ESTIMATION OF SINGLE-INDEX MODELS
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DOI：
10.1016/0304-4076(93)90114-k
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发表时间：
1993-07-01
影响因子：
6.3
通讯作者：
ICHIMURA, H
中科院分区：
文献类型：
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作者：
ICHIMURA, H
对于单指标模型类，我构建了一个系数的半参数估计量，其系数达到乘法常数，表现出 1/平方根 n 一致性和渐近正态性。此类模型包括审查和截断的 Tobit 模型、二元选择模型以及具有未观察到的个体异质性和随机审查的持续时间模型。我还研究了一种实现半参数效率界限的加权方案。
For the class of single-index models, I construct a semiparametric estimator of coefficients up to a multiplicative constant that exhibits 1/square-root n-consistency and asymptotic normality. This class of models includes censored and truncated Tobit models, binary choice models, and duration models with unobserved individual heterogeneity and random censoring. I also investigate a weighting scheme that achieves the semiparametric efficiency bound.