Center-Outward R-Estimation for Semiparametric VARMA Models
Marc Hallin (),
Davide La Vecchia and
No 2019-25, Working Papers ECARES from ULB -- Universite Libre de Bruxelles
We propose a new class of estimators for semiparametric VARMA models with the innovation density playing the role of nuisance parameter. Our estimators are R-estimators based on the multivariate concepts of center-outward ranks and signs recently proposed by Hallin~(2017). We show how these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield a robust yet flexible and powerful class of estimation procedures for multivariate time series. We develop the relevant asymptotic theory of our R-estimators, establishing their root-n consistency and asymptotic normality under a broad class of innovation densities including, e.g. multimodal mixtures of Gaussians or and multivariate skew-t distributions. An implementation algorithm is provided in the supplementary material, available online. A Monte Carlo study compares our R-estimators with the routinely-applied Gaussian quasi-likelihood ones; the latter appear to be quite significantly outperformed away from elliptical innovations. Numerical results also provide evidence of considerable robustness gains. Two real data examples conclude the paper.
Keywords: Multivariate ranks; Distribution-freeness; Local asymptotic normality; Measure transportation; Quasi likelihood estimation; Skew innovation density (search for similar items in EconPapers)
Pages: 45 p.
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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