DP4636 Forecasting Time Series Subject to Multiple Structural Breaks

Author(s):	M Hashem Pesaran, Davide Pettenuzzo, Allan G Timmermann
Publication Date:	September 2004
Keyword(s):	Bayesian model averaging, forecasting, hierarchical hidden Markov Chain Model, structural breaks
JEL(s):	C11, C15, C53
Programme Areas:	Financial Economics
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=4636

This Paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the hyper parameters from the meta distributions that characterize the stochastic break point process. In an application to US Treasury bill rates, we find that the method leads to better out-of-sample forecasts than alternative methods that ignore breaks, particularly at long horizons.