Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factors
通过分散化投影学习潜在因子:因子数高估与弱因子的应用
Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weak serial dependence, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we…