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.
# 11-023/4 (2011-02-08)
- Sjoerd van den Hauwe, Erasmus University Rotterdam; Richard Paap, Erasmus University Rotterdam; Dick J.C. van Dijk, Erasmus University Rotterdam
- Structural breaks, Bayesian analysis, forecasting, MCMC methods, nonlinear time series
- JEL codes:
- C11, C22, C51, C53, C63