We study the fitting of time series models via the minimization of a multi-step-ahead forecast
error criterion that is based on the asymptotic average of squared forecast errors. Our
objective function uses frequency domain concepts, but is formulated in the time domain,
and allows the estimation of all linear processes (eg, ARIMA and component ARIMA). By
using an asymptotic form of the forecast mean squared error, we obtain a well-defined
nonlinear function of the parameters that is proven to be minimized at the true parameter …