报告人：张佳（西南财经大学）

时间：2022年05月19日09:30开始

腾讯会议ID：870 978 644

点击链接入会: https://meeting.tencent.com/dm/VjLFjR4l3c1b 

摘要：Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection. While a large pool of valid moments can potentially improve estimation efficiency, in the meantime a few invalid ones may undermine consistency. This paper investigates the empirical likelihood estimation of these moment-defined models in high-dimensional settings. We propose a penalized empirical likelihood (PEL) estimation and establish its oracle property with consistent detection of invalid moments. The PEL estimator is asymptotically normally distributed, and a projected PEL procedure further eliminates its asymptotic bias and provides more accurate normal approximation to the finite sample behavior. Simulation exercises demonstrate excellent numerical performance of these methods in estimation and inference.

简介：张佳，西南财经大学统计学院讲师，在Journal of Business & Economic Statistics, Journal of Multivariate Analysis等统计学主流期刊上发表多篇论文，正在主持国家自然科学基金青年项目。

邀请人：夏小超

欢迎广大师生积极参与！