【报告摘要】报告摘要：Structural instability has been one of the central research questions in economics and finance over many decades. This paper systematically investigates structural instabilities in high dimensional factor models, which portray both structural breaks and threshold effects simultaneously. The observed high dimensional time series are concatenated at an unknown number of break points, while they are described by multiple threshold factor models that are heterogeneous between any two consecutive subsamples. Both joint and sequential procedures for estimating the break points are developed based on the second moment of the pseudo factor estimates that fully ignore the structural instabilities. In each separated subsample, the group Lasso approach recently proposed by Ma and Tu (2023b) is adopted to efficiently identify the threshold factor structure. An information criterion is further proposed to determine the number of break points, which also serves the purpose to distinguish the two types of instabilities. Theoretical properties of the proposed estimators are established, and their finite sample performance is evaluated in Monte Carlo simulations. An empirical application to the U.S. financial market dataset demonstrates the consequences when structural break meets threshold effect in factor analysis.

【报告人简介】马辰辰，中国科学院数学与系统科学研究院预测科学研究中心助理研究员。主要从事于计量经济学相关领域的理论和应用研究工作。研究方向包括时间序列分析，高维数据分析，因子模型，机器学习方法等。相关研究工作发表在计量经济学国际顶级期刊《Journal of Econometrics》上。