An Alternative Bayesian Approach to Structural Breaks in Time Series Models

Arbeitspapier

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior distribution. Modeling boils down to the choice of a parametric likelihood specification and a baseline prior with the proper support for the parameters. The approach accounts in a natural way for potential out-of-sample breaks where the number of breaks is stochastic. Posterior inference involves simple computations that are less demanding than existing methods. The approach is illustrated on nonlinear discrete time series models and models with restrictions on the parameter space.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 11-023/4

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Model Construction and Estimation ; Forecasting Models; Simulation Methods ; Computational Techniques; Simulation Modeling
• Subject: Structural breaks ; Bayesian analysis ; forecasting ; MCMC methods ; nonlinear time series ; Zeitreihenanalyse ; Strukturbruch ; Bayes-Statistik ; Prognoseverfahren ; Nichtlineares Verfahren ; Markovscher Prozess ; Monte-Carlo-Methode ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): van den Hauwe, Sjoerd ; Paap, Richard ; van Dijk, Dick J.C.
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/86919
• Last update: 10.03.2025, 11:42 AM CET