Estimating the number of latent factors: a comparative analysis
File(s)
Published version (online)
Author(s)
Avarucci, Marco
Raponi, Valentina
Zaffaroni, Paolo
Type
Journal Article
Abstract
This paper evaluates a set of widely used methodologies for determining the number of latent factors in large-dimensional factor models. Its contribution is a comprehensive and systematic comparison of their performance. We assess these estimators not only under the data-generating processes for which they were originally designed, but also across a broader set of environments. Our analysis encompasses static, dynamic, and generalized dynamic factor models, considering factor strength that ranges from strong to semi-strong and semi-weak. Our results show that with strong factors, most estimators across all three classes deliver near-perfect identification when both the cross-section 𝑛 and time dimension 𝑇 are large, providing practitioners with a wide set of reliable choices. As factor strength weakens, performance diverges: only a few estimators remain comparatively robust, while other estimators tend to underestimate the true number of factors or shocks, particularly when the idiosyncratic components are not i.i.d. Overall, no single estimator dominates across all settings. Our findings provide practical guidance for applied work and highlight the advantages and limitations of existing methodologies.
Date Issued
2026-05-13
Date Acceptance
2026-02-27
Citation
Econometric Reviews, 2026
ISSN
0747-4938
Publisher
Taylor and Francis Group
Journal / Book Title
Econometric Reviews
Copyright Statement
© 2026 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
License URL
Identifier
10.1080/07474938.2026.2648946
Publication Status
Published online
Date Publish Online
2026-05-13