Abstract.For predicting the future values of a stationary process {xt} (t= 0, pL 1, pL 2,…) on the basis of its past, two key parameters are the varianceV(h),h≥ 1, of theh‐step prediction error andZ(h) ={R(0) ‐V(h)}/R(0), the corresponding measure, in anR2sense, of the predictability of the process from its past, whereR(0) denotes the process variance. The estimation ofV(h) andZ(h) from a realization ofTconsecutive observations of {xt} is considered, without requiring that the process follows a finite parameter model. Three different autoregressive estimators are examined and are shown to be asymptotically equivalent in the sense that asT∝ they have the same asymptotic normal distribution. The question of bias in estimating these parameters is also examined and a bias correction is proposed. Finite sample behaviour is investigated by a simulat
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