Tail-robust factor modelling of vector and tensor time series in high dimensions
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asaf093 -
Published:
December 26, 2025 -
Added to Tracker:
Feb 10, 2026
Abstract
Summary We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, which produce extreme observations with non-negligible probability. We propose to combine a two-step procedure for tensor decomposition with data truncation, which is easy to implement and does not require an iterative search for a numerical solution. Departing away from the light-tail assumptions often adopted in the time series factor modelling literature, we derive the consistency and asymptotic normality of the proposed estimators while assuming the existence of the (2 + 2ϵ)-th moment only for some ϵ ϵ (0,1). Our rates explicitly depend on ϵ characterizing the effect of heavy tails, and on the chosen level of truncation. We also propose a consistent criterion for determining the number of factors. Simulation studies and applications to two macroeconomic datasets demonstrate the good performance of the proposed estimators.
Author Details
Haeran Cho
AuthorMatteo Barigozzi
AuthorHyeyoung Maeng
AuthorResearch Topics & Keywords
Machine Learning
Research AreaTime Series
Research AreaCitation Information
APA Format
Haeran Cho
,
Matteo Barigozzi
&
Hyeyoung Maeng
(2025)
.
Tail-robust factor modelling of vector and tensor time series in high dimensions.
Biometrika
, 10.1093/biomet/asaf093.
BibTeX Format
@article{paper868,
title = { Tail-robust factor modelling of vector and tensor time series in high dimensions },
author = {
Haeran Cho
and Matteo Barigozzi
and Hyeyoung Maeng
},
journal = { Biometrika },
year = { 2025 },
doi = { 10.1093/biomet/asaf093 },
url = { https://doi.org/10.1093/biomet/asaf093 }
}