Frequency-Band Estimation of the Number of Factors

• 成果类型: Article
• 署名作者: Avarucci, Marco; Cavicchioli, Maddalena; Forni, Mario; Zaffaroni, Paolo
• 署名单位: University of Glasgow; Universita di Modena e Reggio Emilia; Imperial College London; Sapienza University Rome
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2571246
• 发表日期: 2026-04-03
• 页码: 1219-1231
• 关键词: Business cycle; DSGE; Dynamic factors; Frequency bands; Generalized dynamic factor models; Dynamic factor models; business-cycle; principal components; monetary-policy; shocks; real

摘要

We introduce consistent estimators for the number of shocks driving large-dimensional dynamic factor models. Our estimator can be applied to single frequencies and specific frequency bands, making it suitable for disentangling shocks affecting dynamic models with a factor model representation. Noticeably, our estimator requires the time-series and cross-section sizes to diverge simultaneously without any constraint and it is free of nuisance parameters, such as penalization terms. Our methodology appears ideal for macroeconomic analysis, as one can investigate how many shocks drive the business cycle or the long run, although the applicability of our methods is much wider, given the popularity of GDFMs in economics and finance. Its small-sample performance in simulations is excellent. We apply our estimator to the FRED-QD dataset, finding that the U.S. macroeconomy is driven by two shocks: an inflationary demand shock and a deflationary supply shock. Our methodology permits one to accurately estimate the number of shocks that drive medium-sized DSGE models despite their moderate cross-sectional size. Supplementary materials for this article are available online.