Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86919
Authors: 
van den Hauwe, Sjoerd
Paap, Richard
van Dijk, Dick J.C.
Year of Publication: 
2011
Series/Report no.: 
Tinbergen Institute Discussion Paper 11-023/4
Abstract: 
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.
Subjects: 
Structural breaks
Bayesian analysis
forecasting
MCMC methods
nonlinear time series
JEL: 
C11
C22
C51
C53
C63
Document Type: 
Working Paper

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