Identification of the linear factor model
给出了线性因子模型识别的新结果，允许潜在因子相关且异质误差相依，并说明了专用测量结构等约束下的识别，适用于含观测协变量和潜在因子的模型。
This paper provides several new results on identification of the linear factor model. The model allows for correlated latent factors and dependence among the idiosyncratic errors. I also illustrate identification under a dedicated measurement structure and other reduced rank restrictions. I use these results to study identification in a model with both observed covariates and latent factors. The analysis emphasizes the different roles played by restrictions on the error covariance matrix, restrictions on the factor loadings and the factor covariance matrix, and restrictions on the coefficients on covariates. The identification results are simple, intuitive, and directly applicable to many settings.