File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.jeconom.2018.09.003
- Scopus: eid_2-s2.0-85055037698
- PMID: 30546195
- WOS: WOS:000454377800002
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Robust covariance estimation for approximate factor models
Title | Robust covariance estimation for approximate factor models |
---|---|
Authors | |
Keywords | Robust covariance matrix Approximate factor model M-estimator |
Issue Date | 2019 |
Citation | Journal of Econometrics, 2019, v. 208, n. 1, p. 5-22 How to Cite? |
Abstract | In this paper, we study robust covariance estimation under the approximate factor model with observed factors. We propose a novel framework to first estimate the initial joint covariance matrix of the observed data and the factors, and then use it to recover the covariance matrix of the observed data. We prove that once the initial matrix estimator is good enough to maintain the element-wise optimal rate, the whole procedure will generate an estimated covariance with desired properties. For data with bounded fourth moments, we propose to use adaptive Huber loss minimization to give the initial joint covariance estimation. This approach is applicable to a much wider class of distributions, beyond sub-Gaussian and elliptical distributions. We also present an asymptotic result for adaptive Huber's M-estimator with a diverging parameter. The conclusions are demonstrated by extensive simulations and real data analysis. |
Persistent Identifier | http://hdl.handle.net/10722/303583 |
ISSN | 2021 Impact Factor: 3.363 2020 SCImago Journal Rankings: 3.769 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fan, Jianqing | - |
dc.contributor.author | Wang, Weichen | - |
dc.contributor.author | Zhong, Yiqiao | - |
dc.date.accessioned | 2021-09-15T08:25:37Z | - |
dc.date.available | 2021-09-15T08:25:37Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of Econometrics, 2019, v. 208, n. 1, p. 5-22 | - |
dc.identifier.issn | 0304-4076 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303583 | - |
dc.description.abstract | In this paper, we study robust covariance estimation under the approximate factor model with observed factors. We propose a novel framework to first estimate the initial joint covariance matrix of the observed data and the factors, and then use it to recover the covariance matrix of the observed data. We prove that once the initial matrix estimator is good enough to maintain the element-wise optimal rate, the whole procedure will generate an estimated covariance with desired properties. For data with bounded fourth moments, we propose to use adaptive Huber loss minimization to give the initial joint covariance estimation. This approach is applicable to a much wider class of distributions, beyond sub-Gaussian and elliptical distributions. We also present an asymptotic result for adaptive Huber's M-estimator with a diverging parameter. The conclusions are demonstrated by extensive simulations and real data analysis. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Econometrics | - |
dc.subject | Robust covariance matrix | - |
dc.subject | Approximate factor model | - |
dc.subject | M-estimator | - |
dc.title | Robust covariance estimation for approximate factor models | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1016/j.jeconom.2018.09.003 | - |
dc.identifier.pmid | 30546195 | - |
dc.identifier.pmcid | PMC6287924 | - |
dc.identifier.scopus | eid_2-s2.0-85055037698 | - |
dc.identifier.volume | 208 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 5 | - |
dc.identifier.epage | 22 | - |
dc.identifier.eissn | 1872-6895 | - |
dc.identifier.isi | WOS:000454377800002 | - |