SUBVECTOR INFERENCE FOR VARYING COEFFICIENT MODELS WITH PARTIAL IDENTIFICATION
针对由矩等式和/或不等式定义的一类变系数模型，在参数可能非点识别时，提出子向量推断方法并证明其渐近有效性，通过蒙特卡洛模拟和中国2005年1%人口普查数据估计教育回报率验证方法。
This article considers a general class of varying coefficient models defined by a set of moment equalities and/or inequalities, where unknown functional parameters are not necessarily point-identified. We propose an inferential procedure for a subvector of the varying parameters and establish the asymptotic validity of the resulting confidence sets uniformly over a broad family of data-generating processes. We also propose a practical specification test for a set of necessary conditions of our model. Monte Carlo studies show that the proposed methods have good finite sample properties. We apply our method to estimate the return to education in China using its 1%-population census data from 2005.