Estimation in ARMA models based on signed ranks



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.


Volume 2, Issue 2
November 2003
Pages 207-222
  • Receive Date: 23 July 2022
  • Revise Date: 25 May 2024
  • Accept Date: 23 July 2022