Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.09390 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908520447737856 |
|---|---|
| author | Barigozzi, Matteo Cho, Haeran Maeng, Hyeyoung |
| author_facet | Barigozzi, Matteo Cho, Haeran Maeng, Hyeyoung |
| contents | 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 $ε\in (0, 1)$. Our rates explicitly depend on $\eps$ characterising 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tail-robust factor modelling of vector and tensor time series in high dimensions Barigozzi, Matteo Cho, Haeran Maeng, Hyeyoung Methodology 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 $ε\in (0, 1)$. Our rates explicitly depend on $\eps$ characterising 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. |
| title | Tail-robust factor modelling of vector and tensor time series in high dimensions |
| topic | Methodology |
| url | https://arxiv.org/abs/2407.09390 |