Arbeitspapier

Forecasting Time Series Subject to Multiple Structural Breaks

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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 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: IZA Discussion Papers ; No. 1196

Classification: Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Forecasting Models; Simulation Methods

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): Institute for the Study of Labor (IZA)

(where): Bonn

(when): 2004

Handle: http://hdl.handle.net/10419/20442

Last update: 09.07.1253, 2:21 PM CET

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Object type

Arbeitspapier

Associated

Timmermann, Allan
Pettenuzzo, Davide
Pesaran, Mohammad Hashem
Institute for the Study of Labor (IZA)

Time of origin

2004

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