Estimating the Number of Latent Factors: A Comparative Analysis
系统比较了多种估计大维度因子模型中潜在因子数量的方法，评估它们在静态、动态和广义动态模型中的表现，为应用研究者选择可靠方法提供指导。
This paper evaluates a set of widely used methodologies for determining the number of latent factors in large-dimensional factor models. Its contribution is a comprehensive and systematic comparison of their performance. We assess these estimators not only under the data-generating processes for which they were originally designed, but also across a broader set of environments. Our analysis encompasses static, dynamic, and generalized dynamic factor models, considering factor strength that ranges from strong to semi-strong and semi-weak. Our results show that with strong factors, most estimators across all three classes deliver near-perfect identification when both the cross-section n and time dimension T are large, providing practitioners with a wide set of reliable choices. As factor strength weakens, performance diverges: only a few estimators remain comparatively robust, while other estimators tend to underestimate the true number of factors or shocks, particularly when the idiosyncratic components are not i.i.d. Overall, no single estimator dominates across all settings. Our findings provide practical guidance for applied work and highlight the advantages and limitations of existing methodologies.