A hybrid forecasting approach for piece-wise stationary time series

We consider the problem of forecasting a stationary time series when there is an unknown mean break close to the forecast origin. Based on the intercept-correction methods suggested by Clements & Hendry and Bewley, a hybrid approach is introduced, where the break and break point are treated in a Bayesian fashion. The hyperparameters of the priors are determined by maximizing the marginal density of the data. The distributions of the proposed forecasts are derived. Different intercept-correction methods are compared using simulation experiments. Our hybrid approach compares favorably with both the uncorrected and the intercept-corrected forecasts.