An Alternative Bayesian Approach to Structural Breaks in Time Series Models
Sjoerd van den Hauwe
Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)
Erasmus University Rotterdam (EUR) - Department of Econometrics; Tinbergen Institute; Erasmus Research Institute of Management (ERIM)
Dick J. C. van Dijk
Erasmus University Rotterdam - Erasmus School of Economics - Econometric Institute; ERIM
February 7, 2011
Tinbergen Institute Discussion Paper 11-023/4
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.
Number of Pages in PDF File: 49
Keywords: Structural breaks, Bayesian analysis, forecasting, MCMC methods, nonlinear time series
JEL Classification: C11, C22, C51, C53, C63
Date posted: February 10, 2011
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