par Hallin, Marc 
;Swan, Yvik ;Verdebout, Thomas ;Veredas, David
Référence Journal of econometrics, 172, 2, page (195-204)
Publication Publié, 2013
;Swan, Yvik ;Verdebout, Thomas ;Veredas, David Référence Journal of econometrics, 172, 2, page (195-204)
Publication Publié, 2013
Article révisé par les pairs
| Résumé : | Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under stable densities. Contrary to traditional least squares, the proposed R-estimators remain root-n consistent (the optimal rate) under the whole family of stable distributions, irrespective of their asymmetry and tail index. While parametric stable-likelihood estimation, due to the absence of a closed form for stable densities, is quite cumbersome, our method allows us to construct estimators reaching the parametric efficiency bounds associated with any prescribed values (α0,b0) of the tail index α and skewness parameter b, while preserving root-n consistency under any (α,b) as well as under usual light-tailed densities. The method furthermore avoids all forms of multidimensional argmin computation. Simulations confirm its excellent finite-sample performances. © 2012 Elsevier B.V. All rights reserved. |
