Estimating ARMA models efficiently

Abstract

This paper presents the asymptotic and finite sample properties of the efficient method of moments and indirect inference, when applied to estimating stationary ARMA models. Issues such as identification, model selection, and testing are also discussed. The properties of these estimators are compared to those of maximum likelihood using Monte Carlo experiments for both invertible and noninvertible ARMA models.