Center-Outward R-Estimation for Semiparametric VARMA Models
Marc Hallin,
D. La Vecchia and
H. Liu
Journal of the American Statistical Association, 2022, vol. 117, issue 538, 925-938
Abstract:
We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the novel concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam’s asymptotic theory of statistical experiments, yield a class of semiparametric estimation procedures, which are efficient (at a given reference density), root-n consistent, and asymptotically normal under a broad class of (possibly non-elliptical) actual innovation densities. No kernel density estimation is required to implement our procedures. A Monte Carlo comparative study of our R-estimators and other routinely applied competitors demonstrates the benefits of the novel methodology, in large and small sample. Proofs, computational aspects, and further numerical results are available in the supplementary materials. Supplementary materials for this article are available online.
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2020.1832501 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Center-Outward R-Estimation for Semiparametric VARMA Models (2019) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:117:y:2022:i:538:p:925-938
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20
DOI: 10.1080/01621459.2020.1832501
Access Statistics for this article
Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson
More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().