h视频

时间：2026年06月25日10:00—11:30（周四）

地点：中关村校区明德主楼734

主讲人：宛圆渊 多伦多大学h视频 副教授

主持人：洪圣杰副教授

题目：Root-n Asymptotically Normal Maximum Score Estimation

讲座简介：

The maximum score method (Manski, 1975, 1985) is a powerful approach for binary choice models, yet it is known to face both practical and theoretical challenges. In particular, the estimator converges at a slower-than-root-n rate to a nonstandard limiting distribution. We investigate conditions under which strictly concave surrogate score functions can be employed to achieve identification through a smooth criterion function. This criterion enables root-n convergence to a normal limiting distribution. While the conditions to guarantee these desired properties are nontrivial, we characterize them in terms of primitive conditions. Extensive simulation studies support the root-n convergence rate, the asymptotic normality, and the validity of the standard inference methods.

个人简介：

Yuanyuan Wan is an associate professor at the Department of Economics at the University of Toronto. His research interests include causal inference, structural econometrics, partial identification, and quasi-Bayesian inference. Recently, he has been working on designing sharp specification tests for identifying conditions in various causal inference frameworks, including regression discontinuity design, LATE, and MTE. He has published papers in the Journal of Econometrics, Review of Economics and Statistics, Econometric Theory, Quantitative Economics, and Journal of Business and Economic Statistics (JBES) etc. He is currently an associate editor at JBES and Econometric Reviews.