Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?
报告人： Jie Wei（Huazhong University of Science and Technology）
地点：Room 217, Guanghua Building 2
This paper studies the principal component (PC) method-based estimation of weak factor models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for the PC estimators of loadings, which comes from the approximately upper triangular (block) structure of the rotation matrix. It implies an asymmetric relationship among factors: the rotated loadings for a stronger factor can be contaminated by those from a weaker one, but the loadings for a weaker factor is almost free of the impact of those from a stronger one. More importantly, the finding implies that there is no need to use complicated penalties to sparsity the loading estimators. Instead, we adopt a simple screening method to recover the sparsity and construct estimators for various factor strengths. In addition, for sparse weak factor models, we provide a singular value thresholding-based approach to determine the number of factors and establish uniform convergence rates for PC estimators, which complement Bai and Ng (2023). The accuracy and efficiency of the proposed estimators are investigated via Monte Carlo simulations. The application to the FRED-QD dataset reveals the underlying factor strengths and loading sparsity as well as their dynamic features.
About the Speaker:
魏杰，华中科技大学经济学院副教授，美国加州大学河滨分校经济学博士。研究兴趣为面板和因子模型、半参数非参数回归和实证资产定价。论文发表于Oxford Bulletin of Economics and Statistics、Energy Economics和Economics Letters等期刊，主持国家自然科学青年基金和教育部人文社科青年基金项目。
Your participation is warmly welcomed!