编者按：世界计量经济学会2025年“亚洲计量经济学和统计学暑期学校”（2025 Asian Summer School in Econometrics and Statistics）将于2025年7月14日至20日在厦门大学举办，由世界计量经济学会、厦门大学邹至庄经济研究院、中国科学院大学经济与管理学院、中国科学院数学与系统科学研究院预测科学研究中心、东北财经大学高等经济研究院共同主办。本次暑期学校是世界计量经济学会（Econometric Society）2025年在亚洲主办的两个暑期学校之一。

本次暑期学校旨在为海内外经济学、管理学、统计学与相关学科的青年师生介绍计量经济学与统计学领域的国际前沿发展，积极推动计量经济学教育与研究在亚太地区的发展。活动邀请来自美国麻省理工学院、美国南加州大学、美国爱荷华州立大学、美国北卡罗来纳大学夏洛特分校、美国波士顿学院、加拿大不列颠哥伦比亚大学、澳大利亚麦考瑞大学等高校的知名学者亲临授课。课程内容涵盖多个前沿领域，包括但不限于人工智能、机器学习、LASSO、面板数据分析、泡沫检验、金融计量经济学、因果推断等主题。

本次暑期学校不收取注册费，欢迎广大师生报名参加，报名截止日期为2025年5月28日。报名链接：https://conf.xmu.edu.cn/summerschool2025/Call_for_Participation.htm   

Invited Speakers (in alphabetical order):

Jiancheng Jiang, University of North Carolina at Charlotte

Jiancheng Jiang is a Professor of the Department of Mathematics and the School of Data Science of the University of North Carolina at Charlotte, USA. He has been a dedicated educator for many years in both China and the US. He served as the statistics program coordinator at his institute in 2017-2024 and was appointed as Chair Professor of Nankai University in 2017-2020. In addition to chairing various departmental committees, he has served as Associate Editor of Statistics Sinica and Frontiers in Artificial Intelligence and on the editorial boards of several other mathematical, engineering, and statistical journals. Currently, he is serving as Co-PI of the Charlotte Center for Trustworthy AI through Model Management. He has published more than 70 papers in prestigious peer-reviewed journals in statistics and econometrics, including Annals of Statistics, Bernoulli, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, The Econometrics Journal, and Journal of Financial Econometrics. He has also received multiple grants from NSF, NIH, and NSFC. His research interests span a wide range of topics, including Financial Time Series Analysis, Inference for High-dimensional Statistical Models, Distributed and Parallel Computing, Nonparametric Smoothing, Statistical and Machine Learning, Survival Analysis, and Stochastic Processes in Insurance.

Lecture Topics:

1. AI in Science: DNN + Applications

2. Reinforcement Learning with Examples

Vadim Marmer, The University of British Columbia

Vadim Marmer is a Professor of Economics at the University of British Columbia, where he has been a faculty member since 2005. He earned his Ph.D. in Economics from Yale University in 2005, supervised by Donald W.K. Andrews. Professor Marmer’s research primarily focuses on econometrics, covering diverse topics such as estimation and inference in auctions, weak instruments and weak identification, model misspecification, estimation and inference with network-dependent data, time series analysis, and empirical industrial organization. His academic contributions have earned recognition, including being named a Fellow of the Journal of Econometrics and receiving the Journal of Econometrics Arnold Zellner Award. His dedication to teaching excellence has also been acknowledged through the prestigious UBC Killam Teaching Prize. Beyond his research and teaching activities, Professor Marmer actively contributes to academic publishing as an Associate Editor for Econometric Reviews and the Journal of Econometric Methods.

Lecture Topics:

1. Variable Selection Using IC

2. Lasso and Adaptive Lasso Methodologies

3. The Double Lasso Approach

Hyungsik Roger Moon, University of Southern California

Hyungsik Roger Moon is a Professor of Economics at the University of Southern California. He earned his B.A. in Economics from Seoul National University, and his M.A. and Ph.D. in Economics from Yale University. Professor Moon is a Fellow of the Econometric Society, and the Journal of Econometrics. He received the RK Cho Economics Award in 2018, the Maekyung/KAEA Economist Award in 2012, and the Econometric Theory Multa Scripsit Award for 2006-2007. He serves as an Associate Editor for Econometric Theory, Journal of Business and Economic Statistics, and Journal of Econometrics, and the Editor for Ewha Journal of Social Sciences. His work has been published in top academic journals such as Econometrica, Econometric Theory, Journal of Business and Economic Statistics, Journal of Econometrics, and Quantitative Economics. His research interests include econometrics theory and applied econometrics applications to labor, public, IO, macro/finance, and urban economics.

Lecture Topics:

1. Static Linear Panel Regression - Random Effects, Fixed Effects, Correlated Random Effects

2. Dynamic Linear Panel Regression - Nickell Bias, IV and GMM Estimation

3. Panel Regression with Large N, T Asymptotics

4. Interactive Fixed Effects

5. Fixed Effects with Restricted Supports

6. Partially Identified Panel Analysis

7. Sensitivity Analysis

Whitney K. Newey, Massachusetts Institute of Technology

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 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, inference in regression with many covariates, and linear estimation of nonseparable panel models.

Lecture Topics:

1. Introduction to Machine Learning Methods; Lasso, Neural Nets, Random Forests

2. Plug-in Bias and Neyman Orthogonality

3. Estimating the Slope Parameter of a Partially Linear Model; Double Lasso and Cross-Country Growth Regressions

4. Cross-fitting

5. Estimating Average Potential Outcomes and Other Parameters That Depend on a Regression

6. Automatic Debiasing; A Job Training Experiment versus Covariate Controls for the Average Treatment Effect

7. Neyman Orthogonal GMM via Influence Functions with Automatic Debiasing

Shuping Shi, Macquarie University

Shuping Shi is a Professor at Macquarie University. She is an econometrician who specializes in Financial Econometrics and Time Series Analysis. She received the 2020 Discovery Early Career Researcher Award from the Australian Research Council and was honored with the 2022 Young Economist Award by the Economic Society of Australia. Her work has been published in various journals, including Review of Financial Studies, Journal of Econometrics, Management Science, International Economic Review, and Econometric Theory. She is one of the developers of the PSY technique, a real-time monitoring method used by institutions worldwide. She was listed among the top 2% most-cited economists globally in Stanford University's 2024 annual report. She currently serves as an Associate Editor for Econometric Theory and the Journal of Time Series Analysis, and is the inaugural Secretary of the Australian and New Zealand Association of Econometricians.

Lecture Topics:

1. Econometric Techniques for Real-Time Bubble Detection

2. Applications and Extensions: Detecting Speculative Bubbles in Housing Markets

3. Applications and Extensions: Detecting Speculative Bubbles in Financial Markets

4. Econometric Tools for Evaluating Bubble Mitigation Policies

Zhijie Xiao, Boston College

Zhijie Xiao is a Professor of Economics at Boston College. He earned his B.A. (1988) and M.A. (1991) in economics and mathematics from Renmin University, followed by an M.Phil. (1996) and Ph.D. (1997) in economics from Yale University. From 1997 to 2004, he taught at the University of Illinois at Urbana-Champaign, where he served as an Assistant Professor from 1997 to 2002 and an Associate Professor from 2002 to 2004. In 2004, he joined Boston College as a Professor of Economics. He was awarded the National Prize for Science and Technology Progress in China in 1993. He also received the Multa Scripsit Award from Econometric Theory in 2002 and the Plura Scripsit Award from the same journal in 2013. He is a Fellow of the Journal of Econometrics and has served as an Associate Editor for JASA and the Econometrics Journal. He is currently an Associate Editor of Econometric Theory. Zhijie Xiao has published over 100 articles on various topics in econometrics and empirical finance, including time series analysis, quantile regression, operational research, and semiparametric and nonparametric models.

Lecture Topics:

1. Basic Concepts and Methods in Functional Regressions

2. Functional Regression Models

Cindy Yu, Iowa State University

Cindy Yu is a Professor in the Department of Statistics at Iowa State University. She earned her Ph.D. in Statistics from Cornell University in 2005 and has been at Iowa State University since then. Her research focuses on financial statistics, missing data analysis, survey statistics, and causal inference. Prof. Yu has published in journals, including the Journal of the American Statistical Association, Review of Financial Studies, Management Science, Mathematical Finance, Bernoulli, Survey Methodology, Statistica Sinica and American Journal of Agricultural Economics, among others.

Lecture Topic:

Causal Inference as a Missing Data Problem: Foundations and Extensions