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Center-Outward R-Estimation for Semiparametric VARMA Models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Center-Outward R-Estimation for Semiparametric VARMA Models. / Hallin, Marc; La Vecchia, Davide; Liu, Hang.
In: Journal of the American Statistical Association, Vol. 117, No. 538, 30.06.2022, p. 925-938.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hallin, M, La Vecchia, D & Liu, H 2022, 'Center-Outward R-Estimation for Semiparametric VARMA Models', Journal of the American Statistical Association, vol. 117, no. 538, pp. 925-938. https://doi.org/10.1080/01621459.2020.1832501

APA

Hallin, M., La Vecchia, D., & Liu, H. (2022). Center-Outward R-Estimation for Semiparametric VARMA Models. Journal of the American Statistical Association, 117(538), 925-938. https://doi.org/10.1080/01621459.2020.1832501

Vancouver

Hallin M, La Vecchia D, Liu H. Center-Outward R-Estimation for Semiparametric VARMA Models. Journal of the American Statistical Association. 2022 Jun 30;117(538):925-938. Epub 2020 Dec 7. doi: 10.1080/01621459.2020.1832501

Author

Hallin, Marc ; La Vecchia, Davide ; Liu, Hang. / Center-Outward R-Estimation for Semiparametric VARMA Models. In: Journal of the American Statistical Association. 2022 ; Vol. 117, No. 538. pp. 925-938.

Bibtex

@article{bb212e54a9904ab08294e7f0439c9bf9,
title = "Center-Outward R-Estimation for Semiparametric VARMA Models",
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 material.",
keywords = "Multivariate ranks, Distribution-freeness, Local asymptotic normality, Time series, Measure transportation, Quasi likelihood estimation, Skew innovation density",
author = "Marc Hallin and {La Vecchia}, Davide and Hang Liu",
year = "2022",
month = jun,
day = "30",
doi = "10.1080/01621459.2020.1832501",
language = "English",
volume = "117",
pages = "925--938",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "538",

}

RIS

TY - JOUR

T1 - Center-Outward R-Estimation for Semiparametric VARMA Models

AU - Hallin, Marc

AU - La Vecchia, Davide

AU - Liu, Hang

PY - 2022/6/30

Y1 - 2022/6/30

N2 - 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 material.

AB - 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 material.

KW - Multivariate ranks

KW - Distribution-freeness

KW - Local asymptotic normality

KW - Time series

KW - Measure transportation

KW - Quasi likelihood estimation

KW - Skew innovation density

U2 - 10.1080/01621459.2020.1832501

DO - 10.1080/01621459.2020.1832501

M3 - Journal article

VL - 117

SP - 925

EP - 938

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 538

ER -