北大经院工作坊第1202场
Historical anti-fascism and right-wing voting in Italy
经济史工作坊
主讲人：Eik Swee（University of Melbourne）
主持老师：（北大经院）赵一泠
参与老师：
（北大经院）郝煜、管汉晖、周建波
（北大光华）颜色、李波
（清华大学）徐志浩
时间：2025年12月4日（周四）12:00-13:30
地点：北京大学经济学院305会议室
主讲人简介：
Eik Swee is Associate Professor of Economics at the University of Melbourne. He is an applied microeconomist working in the areas of public, political, and development economics. Much of his work examines the causes and consequences of institutional failure, with a particular focus on developing countries. His research spans multiple fields and disciplines (economics, political science, and sociology), and draws on different data sources (observational, spatial, archival, web scraped) to answer policy-relevant questions.
摘要：
We study how anti-fascist opposition during Mussolini’s dictatorship affects post-war support for right-wing parties in Italy. We construct a measure of anti-fascism from the universe of recorded opponents, and use newly digitized historical data to resolve simultaneity between the supply of opposition and the demand for repression, lever-aging the random assignment of judges to the Special Tribunal for the Defense of the State. Stronger local opposition leads to weaker support for right-wing parties decades later. Our model generates predictions about underlying mechanisms, tested using data on collective memorialization and parental voting behavior. The main driver is social transmission of political preferences.
北大经院工作坊第1203场
Structural Properties of Bayesian Updating
微观理论经济学工作坊
主讲人：Kyle Chauvin（Assistant Professor of Economics at NYU Shanghai）
主持老师：
（北大经院）吴泽南、石凡奇
（北大国发院）胡岠
参与老师：
（北大经院）胡涛
（北大国发院）汪浩、邢亦青
（北大光华）翁翕、刘烁
时间：2025年12月4日（周四）10:30-12:00
地点：北京大学经济学院302会议室
主讲人简介：
Kyle Chauvin is an Assistant Professor of Economics at NYU Shanghai. His research in microeconomic theory and behavioral economics investigates the consequences of imperfect learning for communication, persuasion, discrimination, and social networks. He completed his Ph.D. in economics at Princeton University and A.B. in applied mathematics at Harvard University.
摘要：
Using a simple, automaton-like model, this project identifies novel features of Bayesian updating and non-Bayesian heuristics. The model, called a learning rule, combines a set of belief states with a collection of transition functions over the beliefs. It highlights the features of updating that are preserved under belief relabeling. The project's main result characterizes the structure of Bayesian learning rules, in which beliefs are distributions over a latent state and transitions follow Bayes’ rule. A survey of behavioral biases and heuristics identifies many familiar examples as structurally Bayesian and clarifies how and why other cases fail to be so.
北大经院工作坊第1204场
Out-of-Sample Sharpe Estimation and Volatility Proxy Construction
Chinese Title（样本外夏普比率估计与波动率鲁棒代理构造）
计量、金融和大数据分析工作坊
主讲人：Weichen Wang（The University of Hong Kong）
主持老师：（北大经院）王一鸣、王法
参与老师：（北大经院）王熙、刘蕴霆、巩爱博
时间：2025年12月5日（周五）10:00-11:30
地点：北京大学经济学院107会议室
主讲人简介：
Prof Weichen Wang joined The University of Hong Kong in 2021 as an Assistant Professor in the area of Innovation and Information Management of HKU Business School. Weichen obtained his PhD in Operations Research and Financial Engineering from Princeton University in 2016, supervised by Prof Jianqing Fan. After graduation, Weichen joined Two Sigma Investments as a quantitative researcher. He also served as a visiting lecturer at Princeton University for Spring 2020. Before PhD, Weichen received his bachelor’s degree in Mathematics and Physics from Tsinghua University in 2011. Prof Wang’s research combine statistics, econometrics and machine learning techniques, and find applications in portfolio management and financial science. He is particularly interested in the factor structure of the financial market and real-world applications of machine learning. His works have been published in Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Journal of Econometrics, Operations Research etc.
摘要：
This talk will contain two parts. The first half is about estimation of out-of-sample Sharpe ratio for high dimensional portfolio optimization, and the second half is about an empirical deviation perspective on volatility proxy construction.
Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. We propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. Furthermore, portfolio managers can use the estimated out-of-sample Sharpe as a criterion to decide the best tuning for constructing their portfolios. We demonstrate the effectiveness of our approach through comprehensive simulations and real data experiments.
Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this talk, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data, and the third of which is based on exponentially weighted Huber loss minimization. Finally, we exploit the proposed robust volatility proxy to compare different volatility predictors on the Bitcoin market data. When the sample size is limited, applying the robust volatility proxy gives more consistent and stable evaluation of volatility forecasts.
北大经院学术午餐会
第207期
碳中和目标下的中国森林碳汇路径——来自市场驱动和森林经营的视角
主讲人：田晓晖（中国人民大学农业与农村发展学院教授）
评论人：杨青（上海财经大学财税投资学院副教授）
主持老师：高明（北京大学经济学院长聘副教授）
时间：2025年12月5日（周五）12:30-14:00
地点：北京大学经济学院107会议室
主讲人简介：
田晓晖，中国人民大学农业与农村发展学院教授，兼任校党委教师工作部（人才工作领导小组办公室）副部长（副主任），入选国家级青年人才计划，受聘中国人民大学吴玉章青年学者。美国俄亥俄州立大学农业经济学博士。研究聚焦于土地利用部门的环境与气候政策。研究成果发表于American Journal of Agricultural Economics、Land Economics、Journal of Money, Credit and Banking等期刊。主持多项国家自然科学基金项目。
摘要：
在中国“双碳”目标背景下，森林部门在实现碳中和方面发挥着关键作用。然而，目前关于市场和管理因素如何影响森林碳通量的定量研究仍然较少。本研究构建了一个中国林业部门的局部均衡模型，并基于最新的森林资源清查数据进行校准，以预测木材市场反应及碳汇路径。研究结果表明，2020年至2060年间，森林碳汇年均水平为164 Tg C yr-1，并在本世纪末前维持在约100 Tg C yr-1的水平，同时木材供应持续增长。更高强度的商品林经营管理是碳汇增加的主要推动力，到2060年将贡献总森林碳汇的51%。情景分析显示，如果将管理强度维持在当前水平，到2060年的年碳汇量将减少约15%。研究结果突显了木材市场需求和森林管理在基于自然的气候解决方案中的核心作用。
供稿：科研与博士后办公室
美编：闻听
责编：度量、雨禾、雨田
