Econometric Analysis of Large Factor Models
综述了大因子模型的估计与统计推断理论，该模型用少量潜在因子刻画高维经济变量的共同变动，能有效总结大数据信息，用于改进预测、控制未观测异质性等，对经济学和金融学研究者有参考价值。
Large factor models use a few latent factors to characterize the co-movement of economic variables in a high-dimensional data set. High dimensionality brings challenges as well as new insights into the advancement of econometric theory. Because of their ability to effectively summarize information in large data sets, factor models have been increasingly used in economics and finance. The factors, estimated from the high-dimensional data, can, for example, help improve forecasting, provide efficient instruments, control for nonlinear unobserved heterogeneity, and capture cross-sectional dependence. This article reviews the theory on estimation and statistical inference of large factor models. It also discusses important applications and highlights future directions.