Center-Outward R-Estimation for Semiparametric VARMA Models
| Autor: | Hang Liu, Davide La Vecchia, Marc Hallin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Estimation Local asymptotic normality 05 social sciences Estimator Mathematics - Statistics Theory Statistics Theory (math.ST) 01 natural sciences 010104 statistics & probability 0502 economics and business FOS: Mathematics Nuisance parameter Applied mathematics théorie et applications [Econométrie et méthodes statistiques] Multivariate ranks Distribution-freeness Local asymptotic normality Measure transportation Quasi likelihood estimation Skew innovation density Center (algebra and category theory) 0101 mathematics Statistics Probability and Uncertainty 050205 econometrics Mathematics |
| Zdroj: | ECARES Working Papers; 2019-25 |
| DOI: | 10.6084/m9.figshare.13056172 |
| Popis: | 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. info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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