Forecasting Time Series Subject to Multiple Structural Breaks

Mohammad Pesaran, Davide Pettenuzzo () and Allan Timmermann

No 1237, CESifo Working Paper Series from CESifo

Abstract: 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.

Keywords: structural breaks; forecasting; hierarchical hidden Markov chain model; Bayesian model averaging. (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp1237.pdf (application/pdf)

Related works:
Journal Article: Forecasting Time Series Subject to Multiple Structural Breaks (2006)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_1237

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().