TY  - RPRT
        AU  - Pesaran, M. Hashem
AU  - Pettenuzzo, Davide
AU  - Timmermann, Allan
        TI  - Forecasting Time Series Subject to Multiple Structural Breaks
        PY  - 2004/Jun/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 1196
        UR  - https://www.iza.org/publications/dp1196
        AB  - 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.
        KW  - hierarchical hidden Markov chain model
KW  - forecasting
KW  - structural breaks
KW  - Bayesian model averaging
        ER  -