FEASIBLE CROSS‐VALIDATORY MODEL SELECTION FOR GENERAL STATIONARY PROCESSES

Cross‐validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross‐validation is viewed as being computationally expensive in many situations. In this paper it is shown that the h‐block cross‐validation function for least‐squares based estimators can be expressed in a form which can enormously impact on …