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.

Bayesian analysis, MCMC methods, nonlinear time series, structural breaks
Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C22), Model Construction and Estimation (jel C51), Forecasting and Other Model Applications (jel C53), Computational Techniques; Simulation Modelling (jel C63)
Tinbergen Institute
Tinbergen Institute Discussion Paper Series
Discussion paper / Tinbergen Institute
Tinbergen Institute

van den Hauwe, S, Paap, R, & van Dijk, D.J.C. (2011). An Alternative Bayesian Approach to Structural Breaks in Time Series Models (No. TI 2011-023/4). Discussion paper / Tinbergen Institute. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/22551