Robust Estimation for Number of Factors in High Dimensional Factor Modeling via Spearman Correlation Matrix
针对高维因子模型中数据重尾导致因子数量难以确定的问题，提出基于斯皮尔曼样本相关矩阵谱性质的稳健估计量，在维度和样本量同比例增长时具有一致性，数值实验优于现有方法。
Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this article, we introduce a new estimator based on the spectral properties of Spearman sample correlation matrix under the high-dimensional setting, where both dimension and sample size tend to infinity proportionally. Our estimator is robust against heavy tails in either the common factors or idiosyncratic errors. The consistency of our estimator is established under mild conditions. Numerical experiments demonstrate the superiority of our estimator compared to existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.