Inference in Group Factor Models With an Application to Mixed-Frequency Data

• 成果类型: Article
• 署名作者: Andreou, E.; Gagliardini, P.; Ghysels, E.; Rubin, M.
• 署名单位: University of Cyprus; Center for Economic & Policy Research (CEPR); Universita della Svizzera Italiana; Swiss Finance Institute (SFI); University of North Carolina; University of North Carolina Chapel Hill; Universite Catholique de Lille; EDHEC Business School
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14690
• 发表日期: 2019
• 页码: 1267-1305
• 关键词: PRINCIPAL COMPONENTS; number; common; tests; rank; arbitrage; shocks; panel

摘要

We derive asymptotic properties of estimators and test statistics to determine-in a grouped data setting-common versus group-specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit canonical correlations), we derive a parameter-free asymptotic Gaussian distribution. We show how the group factor setting applies to mixed-frequency data. As an empirical illustration, we address the question whether Industrial Production (IP) is still the dominant factor driving the U.S. economy using a mixed-frequency data panel of IP and non-IP sectors. We find that a single common factor explains 89% of IP output growth and 61% of total GDP growth despite the diminishing role of manufacturing.