Bayesian Estimation of the Eigenstructure in High-Dimensional Approximate Factor Models
高维近似因子模型中特征结构的贝叶斯估计
High-dimensional economic datasets often display strong co-movement driven by a small number of latent factors, which are typically modeled using approximate factor models. When the number of variables is large relative to the sample size, the eigenvalues and eigenvectors of the sample covariance matrix are severely distorted, which in turn makes principal component based estimators of the factor structure unstable. To address the high-dimensional problem, we propose a…