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‘Forecasting Time Series Subject to Multiple Structural Breaks’ Author info | Abstract | Publisher info | Download info | Related research | Statistics Pesaran, M.H.
Pettenuzzo, D.
Timmermann, A.
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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 characterise 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.
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Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number
0433.
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Length: 43
Date of creation: Jun 2004Date of revision:
Handle: RePEc:cam:camdae:0433Note: EMContact details of provider: Web page: http://www.econ.cam.ac.uk/index.htm
For technical questions regarding this item, or to correct its listing, contact: (Howard Cobb).
Keywords: structural breaks ; forecasting ; hierarchical hidden Markov Chain model ; Bayesian model averaging ; Other versions of this item:
Article Paper Pesaran, M. Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
IZA Discussion Papers
1196, Institute for the Study of Labor (IZA).
[Downloadable!] Pesaran, M Hashem & Pettenuzzo, Davide & Timmermann, Allan G, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
CEPR Discussion Papers
4636, C.E.P.R. Discussion Papers.
[Downloadable!] (restricted) M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
CESifo Working Paper Series
CESifo Working Paper No. , CESifo Group Munich.
[Downloadable!] Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
This paper has been announced in the following NEP Reports :
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Pesaran, M Hashem & Timmermann, Allan G, 2004.
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