Local Partitioned Regression
提出一种名为局部分区回归的核估计方法，用于非参数模型，具有最优收敛速度和计算简便等优点，并通过蒙特卡洛模拟验证其效果。
In this paper, we introduce a kernel-based estimation principle for nonparametric models named local partitioned regression (LPR). This principle is a nonparametric generalization of the familiar partition regression in linear models. It has several key advantages: First, it generates estimators for a very large class of semi- and nonparametric models. A number of examples that are particularly relevant for economic applications will be discussed in this paper. This class contains the additive, partially linear, and varying coefficient models as well as several other models that have not been discussed in the literature. Second, LPR-based estimators achieve optimality criteria: They have optimal speed of convergence and are oracle-efficient. Moreover, they are simple in structure, widely applicable, and computationally inexpensive. A Monte Carlo simulation highlights these advantages. Copyright The Econometric Society 2006.