Estimation in ARMA models based on signed ranks

Abstract. In this paper we develop an asymptotic theory for estima-tion 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 perfor-mance of the proposed estimators via a Monte Carlo study.