High-Dimensional Statistical Inferences with Over-Identification

• Activity Number: 626 - Recent Advances in High-Dimensional Statistical Inference
• Type: Invited
• Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Jinyuan Chang* and Song Xi Chen and Cheng Yong Tang and Tong Tong Wu
• Companies: Southwestern University of Finance and Economics and Peking University and Temple University and University of Rochester
• Keywords: Empirical likelihood; estimating equations; generalized method of moments; high-dimensional statistical inferences; over-identification

Abstract

Over-identification is a signature feature of the influential Generalized Method of Moments (Hansen, 1982) that flexibly allows more moment conditions than the model parameters. Investigating over-identification together with high-dimensional statistical problems is challenging and remains less explored. In this talk, we study two high-dimensional statistical problems with over-identification. The first one concerns statistical inferences associated with multiple components of the high-dimensional model parameters, and the second one is on developing a specification test for assessing the validity of the over-identified moment conditions.