Twicing kernels and a small bias property of semiparametric estimators
成果类型：
Article
署名作者：
Newey, WK; Hsieh, F; Robins, JM
署名单位：
Massachusetts Institute of Technology (MIT); University of California System; University of California Davis; Harvard University; Harvard T.H. Chan School of Public Health
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.1111/j.1468-0262.2004.00518.x
发表日期：
2004
页码：
947-962
关键词：
摘要：
The purpose of this note is to show how semiparametric estimators with a small bias property can be constructed. The small bias property (SBP) of a semiparametric estimator is that its bias converges to zero faster than the pointwise and integrated bias of the nonparametric estimator on which it is based. We show that semiparametric estimators based on twicing kernels have the SBP. We also show that semiparametric estimators where nonparametric kernel estimation does not affect the asymptotic variance have the SBP. In addition we discuss an interpretation of series and sieve estimators as idempotent transformations of the empirical distribution that helps explain the known result that they lead to the SBP. In Monte Carlo experiments we find that estimators with the SBP have mean-square error that is smaller and less sensitive to bandwidth than those that do not have the SBP.
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