Prediction Intervals

Computing prediction intervals (P.I.s) is an important part of the forecasting process intended s i to indicate the likely uncertainty in point forecasts. The commonest method of calculating P.I. s to use theoretical formulae conditional on a best-fitting model. If a normality assumption is t o used, it needs to be checked. Alternative computational procedures that are not so dependen n a fitted model include the use of empirically based and resampling methods. Some sos called approximate formulae should be avoided. P.I.s tend to be too narrow because out-of ample forecast accuracy is often poorer than would be expected from within-sample fit, i particularly for P.I.s calculated conditional on a model fitted to past data. Reasons for this nclude uncertainty about the model and a changing environment. Ways of overcoming these m problems include using a mixture of models with a Bayesian approach and using a forecasting ethod that is designed to be robust to changes in the underlying model. ; P