Loading...
One-step R-estimation in linear models with stable errors
Hallin, Marc Swan, Yvik Verdebout, Thomas Veredas, David
Hallin, Marc
Swan, Yvik
Verdebout, Thomas
Veredas, David
Citations
Altmetric:
Publication Type
Journal article
Editor
Supervisor
Publication Year
2013-02
Journal
Journal of Econometrics
Book
Publication Volume
172
Publication Issue
2
Publication Begin page
195
Publication End page
204
Publication Number of pages
Collections
Abstract
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.
Research Projects
Organizational Units
Journal Issue
Keywords
49 Mathematical Sciences, 38 Economics, 4905 Statistics, 3802 Econometrics