出版社：Suntory Toyota International Centre for Economics and Related Disciplines
摘要：In semiparametric models it is a common approach to under-smooth the
nonparametric functions in order that estimators of the finite dimensional
parameters can achieve root-n consistency. The requirement of under-smoothing
may result as we show from inefficient estimation methods or technical difficulties.
Based on local linear kernel smoother, we propose an estimation method to
estimate the single-index model without under-smoothing. Under some conditions,
our estimator of the single-index is asymptotically normal and most efficient in the
semi-parametric sense. Moreover, we derive higher expansions for our estimator
and use them to define an optimal bandwidth for the purposes of index estimation.
As a result we obtain a practically more relevant method and we show its superior
performance in a variety of applications.
