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 and Hendry (1998) and Bewley (2003), 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. Copyright © 2006 John Wiley & Sons, Ltd.
Volume (Year): 25 (2006)
Issue (Month): 7 ()
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