High Dimensional Mixed Frequency Factor Models with Temporal Aggregation

王法，北京大学经济学院金融系副教授，博士生导师，美国雪城大学经济学博士。加入北京大学前，曾在伦敦大学卡斯商学院和上海财经大学担任助理教授。研究领域为金融计量，因子模型，因果推断和高维计量经济学，主讲时间序列、固定收益证券等课程。研究成果多次发表于Journal of Econometrics和Econometric Reviews等期刊，并多次担任经济学国际一流期刊的审稿人。 

In mixed-frequency data sets, observed low-frequency series are generated from latent high-frequency processes through temporal aggregation that depend on whether the underlying variables are stocks or flows. This paper develops a high-dimensional mixed-frequency factor model that explicitly incorporates the temporal aggregation relationship within a unified factor structure. We propose a mixed-frequency EM estimator and establish the consistency, convergence rates, and limiting distributions for the estimated factors and loadings. Monte Carlo simulations show that the proposed estimator accurately recovers latent high-frequency factors and common components, and substantially outperforms methods that treat mixed-frequency observations merely as missing data across different error structures and aggregation schemes. An application to U.S. macroeconomic data constructs a monthly measure of U.S. GDP growth and demonstrates the usefulness of the proposed procedure for nowcasting quarterly U.S. GDP growth.