近日，我院教师张应麟作为独立作者撰写的论文Identification of time-varying factor models在国际A+期刊Journal of Business & Economic Statistics，2024年第42期发表。

 

文章概览

The emergence of large datasets with long time spans has cast doubt on the assumption of constant loadings in conventional factor models. Being a potential solution, the time-varying factor model (TVFM) has attracted enormous interests in the literature. However, TVFM also suffers from the well-known problem of nonidentifiability. This article considers the situations under which both the factors and factor loadings can be estimated without rotations asymptotically. Asymptotic distributions of the proposed estimators are derived. Theoretical findings are supported by simulations. Finally, we evaluate the forecasting performance of the estimated factors subject to different identification restrictions using an extensive dataset of the U.S. macroeconomic variables. Substantial differences are found among the choices of identification restrictions.

期刊信息

Journal of Business & Economic Statistics创办于1983年，是一本主要发布商业和经济领域相关的统计学研究的顶级学术期刊。该期刊涉及的主题包括但不限于经济建模、计量经济学、金融统计等，特别关注使用统计方法来解决实际的商业和经济问题。它的目标是为学术界、政策制定者和业界提供创新的统计方法和实证研究。期刊内容包括理论研究、应用研究以及方法论的进展，致力于推动统计学在经济学、商业和社会科学中的应用与发展。

 

文章链接

https://doi.org/10.1080/07350015.2022.2151449

 

作者介绍

张应麟，首都经济贸易大学国际经济管理学院副教授。研究方向主要包括高维因子模型、多维度数据分析及时间序列分析。论文发表于Journal of Business & Economic Statistics、Journal of Economic Behavior & Organization和Economics Letters等国际权威期刊。多次担任Journal of the American Statistical Association、Journal of Business & Economic Statistics及Journal of Econometrics等顶级期刊匿名审稿人。主持自然科学基金青年项目课题。