Arbeitspapier

Forecasting time series subject to multiple structural breaks

to connected objects

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.

Language
Englisch

Bibliographic citation
Series: CESifo Working Paper ; No. 1237

Classification
Wirtschaft
Forecasting Models; Simulation Methods
Bayesian Analysis: General
Statistical Simulation Methods: General
Subject
structural breaks
forecasting
hierarchical hidden Markov chain model
Bayesian model averaging
Prognoseverfahren
Zeitreihenanalyse
Strukturbruch
Theorie

Event
Geistige Schöpfung
(who)
Timmermann, Allan
Pettenuzzo, Davide
Pesaran, Mohammad Hashem
Event
Veröffentlichung
(who)
Center for Economic Studies and ifo Institute (CESifo)
(where)
Munich
(when)
2004

Handle
Last update
10.03.2025, 11:41 AM CET

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Object type

  • Arbeitspapier

Associated

  • Timmermann, Allan
  • Pettenuzzo, Davide
  • Pesaran, Mohammad Hashem
  • Center for Economic Studies and ifo Institute (CESifo)

Time of origin

  • 2004

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