论文标题
短面板中潜在因素数量的特征值测试
Eigenvalue tests for the number of latent factors in short panels
论文作者
论文摘要
本文研究了具有较小时间维度的大型横截面因子模型中潜在因素数量的新测试。这些测试基于（可能是加权）资产返回的方差 - 可协方差矩阵的特征值，并依赖于球形误差的假设或仪器变量用于因子beta。我们使用基于扰动理论的对称矩阵的膨胀定理建立渐近分布结果。我们的框架适合系统组件中的半突变因素。我们提出了针对强或半肌因素的弱因素的新统计检验。我们为我们的权益数据提供了经验应用。根据市场低迷和市场上升的不同潜在因素的证据在被考虑的子周期中统计上是模棱两可的。特别是，我们的结果与熊市中单个因素模型的共同智慧矛盾。
This paper studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance-covariance matrices of (possibly weighted) asset returns, and rely on either the assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to US equity data. Evidence for a different number of latent factors according to market downturns and market upturns, is statistically ambiguous in the considered subperiods. In particular, our results contradicts the common wisdom of a single factor model in bear markets.
