来源：市场资讯
（来源：深圳高等金融研究院）
不可分面板数据下结构与因果效应的线性估计
Linear Estimation of Structural and Causal Effects for Non-separable Panel Data
讲座信息
Seminar Information
主讲人
Speaker
Whitney K. Newey 教授
麻省理工学院
Professor Whitney K. Newey
Massachusetts Institute of Technology
日期和时间
Date and Time
2026年7月8日（周三）
15:30-17:00
July 8, 2026 (Wednesday)
3:30 pm-5:00 pm
地点
Venue
综合教学楼D904会议室
Room 904, Teaching Complex D Building
讲座概述
Abstract
This paper develops linear estimators for structural and causal parameters in nonparametric, non-separable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of non-separable functions by linear sieve specifications with individual-specific parameters. Effects of interest are estimated by a bias corrected average of individual ridge regressions.
We demonstrate how this approach can be applied to estimate causal effects, counterfactual consumer welfare, and averages of individual taxable income elasticities. We show that the proposed estimator has an empirical Bayes interpretation and possesses a number of other useful properties. We formulate large-T asymptotics that can accommodate discrete regressors and which bypass partial identification in this case. We employ the methods to estimate average equivalent variation and deadweight loss for potential price increases using data on grocery purchases.
主讲人简介
About the Speaker
Whitney K. Newey 教授
麻省理工学院
Whitney K. Newey现任麻省理工学院经济学福特讲席教授，是2026年经济学欧文·普莱因·内默斯奖（Erwin Plein Nemmers Prize in Economics）获得者。他同时担任美国经济协会杰出会士、美国艺术与科学院院士，以及计量经济学会会士。Newey教授在诸多计量经济学领域做出重要贡献，包括方差估计量、非参数联立方程、动态与非线性面板估计、依赖于未知函数的半参数估计、一般异质性条件下的消费者剩余估计，以及去偏机器学习等。相关成果已发表于《Econometrica》、《Journal of Political Economy》、《The Review of Economic Studies》、《Journal of the American Statistical Association》、《Journal of Econometrics》等国际顶尖学术期刊。他目前的研究方向包括：去偏机器学习、不可分面板模型的线性估计，以及面板数据中的经济需求估计。
*Please scroll down for the English version
Whitney K. Newey is the Ford Professor of Economics at the Massachusetts Institute of Technology. He is the recipient of the 2026 Erwin Plein Nemmers Prize in Economics. He is also a Distinguished Fellow of the American Economic Association, Member of the American Academy of Arts and Sciences, and a Fellow of the Econometric Society. Professor Newey has contributed to the development of variance estimators, nonparametric simultaneous equations, dynamic and nonlinear panel estimation, semiparametric estimation depending on unknown function, consumer surplus estimation with general heterogeneity, and debiased machine learning. He has published extensively on these and other topics in top academic journals such as Econometrica, Journal of Political Economy, The Review of Economic Studies, Journal of the American Statistical Association, and the Journal of Econometrics. His current research interests include debiased machine learning, linear estimation of non-separable panel models, and economic demand estimation in panel data.
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