Efficient semiparametric estimation via moment restrictions

• 成果类型: Article
• 署名作者: Newey, WK
• 署名单位: Massachusetts Institute of Technology (MIT)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2004.00557.x
• 发表日期: 2004
• 页码: 1877-1897
• 关键词: empirical likelihood; generalized-method; regression; models; gmm

摘要

Conditional moment restrictions can be combined through GMM estimation to construct more efficient semiparametric estimators. This paper is about attainable efficiency for such estimators. We define and use a moment tangent set, the directions of departure from the truth allowed by the moments, to characterize when the semiparametric efficiency bound can be attained. The efficiency condition is that the moment tangent set equals the model tangent set. We apply these results to transformed, censored, and truncated regression models, e.g., finding that the conditional moment restrictions from Powell's (1986) censored regression quantile estimators can be combined to approximate efficiency when the disturbance is independent of regressors.