Estimating Stationary ARMA Models Efficiently

Abstract

This paper discusses the asymptotic and finite-sample properties of the Efficient Method of Moments (EMM) when applied to estimating stationary ARMA models. Issues such of identification, model selection, and testing are also discussed. The properties of these estimators are compared to those of Maximum Likelihood (ML) by means of Monte Carlo experiments for bot invertible and non-invertible ARMA models.