Authors

• J. Allal
• A. Kaaouachi

Abstract

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performance of the proposed estimators via a Monte Carlo study.

Keywords

• ARMA models
• asymptotic relative efficiency
• asymptotic uniform linearity
• local asymptotic normality
• rank and signed-rank procedures
• R-estimation
• serial signed rank statistics