【报告摘要】Threshold factor models are pivotal for capturing rapid regime-switching dynamics in high-dimensional time series, yet existing frameworks relying on a single pre-specified threshold variable often suffer from model misspecification and unreliable inferences. This paper introduces a novel factor tree model that integrates classification and regression tree (CART) principles with high-dimensional factor analysis to address structural instabilities driven by multiple threshold variables. The factor tree is constructed via a recursive sample splitting procedure that maximizes reductions in a loss function derived from the second moments of estimated pseudo linear factors. This procedure terminates when a data-driven information criterion signals no further improvement. To mitigate overfitting, a node merging algorithm further consolidates leaf nodes with identical factor representations. Theoretical analysis establishes consistency in threshold variable selection, threshold estimation, and factor space recovery, supported by extensive Monte Carlo simulations. An empirical application to U.S. financial data demonstrates the factor tree's effectiveness in capturing regime-dependent dynamics.

 【报告人介绍】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，北京大学统计学博士（2024年）。主要研究方向为计量经济学、时间序列分析、高维因子模型、结构变点和门限效应估计等。研究成果发表于Journal of Econometrics, Statistical Science, The Annals of Applied Statistics等学术期刊，并主持国自然青年科学基金项目。