Comparing Cross-Section and Time-Series Factor Models
使用Fama-MacBeth回归方法构建与Fama-French五因子模型对应的横截面因子，发现仅用横截面因子的时间序列模型比用时间序列因子的模型能更好描述平均收益率。
Abstract We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.