讲座题目： A Tale of Two Types of Structural Instabilities in High Dimensional Factor Models 高维因子模型中的两种结构不稳定性

主讲人：涂云东   北京大学光华管理学院和北京大学统计科学中心   联席教授

讲座地点：bv韦德B224

讲座时间：2023年7月20日9:30-11:30

讲座内容摘要：

With the increasing availability of large data sets in economics and finance, the large factor model has become one of the most important tools to achieve dimension reduction in the statistical and econometric analysis. To capture the instability caused by economic condition shifts or policy reforms, factor models with structural breaks in the factor loadings are accordingly developed. On the other hand, recurring regime shifts that relate to higher frequency recurring fluctuation arise in situation where “history repeats”, and are conveniently described by threshold factor models, which allow recurring regime shifts in the factor loadings according to the magnitude of a (continuous) threshold variable. In practice, it is often difficult to decide whether structural break or threshold effect, or both types of instabilities one should employ to portray the observed data. This talk shall discuss how to model each type of instability in factor analysis separately first, and then provide a solution to distinguish the two categories in a model that simultaneously allow both types of structural instabilities. The proposed models are estimated by machine learning techniques such as group Lasso, backward elimination algorithms and information criterion-based model selection methods. The associated asymptotic properties are established and are corroborated by finite sample simulation results and empirical examples. This talk is based on joint projects with Chenchen Ma, who is currently a Ph.D. candidate at the Center of Statistical Science, Peking University.

随着经济学和金融学中大量数据集的出现，高维因子模型已经成为统计和计量经济分析中实现降维的最重要工具之一。为了捕捉由经济条件变化或政策改革引起的不稳定性，相应地开发了在因子載荷中具有结构突变的因子模型。另一方面，在“历史重复”的情况下，会出现与较高频率循环波动相关的循环状态变化，这可以通过阈值因子模型方便地描述，该模型允许根据(连续)阈值变量的大小，在因子负荷中出现循环状态变化。在实践中，通常很难决定是结构突变还是阈值效应，或者两种类型的不稳定性都应该用来描述观察到的数据。本讲座将首先讨论如何在因子分析中分别模拟每种类型的不稳定性，然后提供一个解决方案来区分同时允许两种类型的结构不稳定性的模型中的两个类别。所提出的模型通过机器学习技术来估计，例如组套索、向后消除算法和基于信息准则的模型选择方法。本文建立了相关的渐近性质，并通过有限样本模拟结果和实证例子得到了证实。本次演讲基于与马辰辰的合作项目，他目前是北京大学统计科学中心的博士生。

主讲人学术简介：

涂云东，北京大学光华管理学院和北京大学统计科学中心联席教授。入选“日出东方”北大光华青年人才，北京大学优秀博士学位论文指导教师，教育部青年人才入选者。2004年和2006年先后获bv韦德理学学士学位和经济学硕士学位，2012年获美国加州大学河滨分校经济学博士学位。亚太青年计量经济学者会议发起人和组织者。40余篇学术论文发表在多个国际国内知名专业杂志，包括计量经济学国际顶级期刊Journal of Econometrics 9篇。主持多个国家自然科学基金项目，并担任自然科学基金匿名评审。曾获世界计量经济学会、加州计量经济学会议等学术组织提供的青年学者研究资助。研究领域涵盖时间序列分析、非参数计量方法、大数据分析、金融计量和预测等。