【CEA 2026】主旨演讲嘉宾 MIT 教授Whitney Newey
2026年6月12日-14日，全英/全欧中国经济学会（The Chinese Economic Association UK/Europe, CEA）2026年中国学术年会将在北京召开。会议以“迈向智能、绿色与融合的经济学”为主题，旨在探讨智能、绿色、融合背景下经济学的前沿问题与产业实践，促进跨学科创新与人才培养高质量发展。本次会议由中国科学院数学与系统科学研究院预测科学研究中心，中国科学院大学经济与管理学院，厦门大学邹至庄经济研究院、经济学院与王亚南经济研究院，中央财经大学创新发展学院承办。本次会议共邀请8位全球杰出学者发表主旨演讲。
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1演讲嘉宾：Whitney K. Newey
Whitney K. Newey is the Ford Professor of Economics at Massachusetts Institute of Technology. He is a Distinguished Fellow of the American Economic Association, Member of the American Academy of Arts and Sciences, and a Fellow of the Econometric Society. He is the recipient of the 2026 Erwin Plein Nemmers Prize in Economics. He also served as Co-editor of Econometrica, as Program Co-chair for the 2005 World Congress of the Econometric Society, and on the Executive Committee of the Econometric Society. Professor Newey is best known for his contribution to the development of the Newey-West estimator of the variance of estimators in the presence of autocorrelation and heteroskedasticity. He has also contributed to the development of other important econometric techniques, such as nonparametric instrumental variable identification and estimation, dynamic or nonlinear panel data models, and semiparametric estimation depending on unknown functions. 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 nonseparable panel models, and economic demand estimation in panel data.
演讲题目：Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data
演讲摘要：This paper develops linear estimators for structural and causal parameters in nonparametric, nonseparable 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 by a linear sieve 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.
