Generalized Least Squares Estimation of ARMA Models

Two multistage methods for estimating scalar ARMA models are investigated. Both estimate innovations using an autoregression; these are used to obtain initial ARMA parameter estimates by regression and finally the initial estimates are refined by generalized least squares or nonlinear optimization to achieve efficiency. We provide a proof of the generalized least squares procedure.