Bayesian Model Selection for Structural Break Models

46 Pages Posted: 15 May 2008

See all articles by Andrew T. Levin

Andrew T. Levin

affiliation not provided to SSRN

Jeremy Piger

University of Oregon - Department of Economics

Abstract

We take a Bayesian approach to model selection in regression models with structural breaks in conditional mean and residual variance parameters. A novel feature of our approach is that it does not assume knowledge of the parameter subset that undergoes structural breaks, but instead conducts model selection jointly over the number of structural breaks and the subset of the parameter vector that changes at each break date. Simulation experiments demonstrate that conducting this joint model selection can be quite important in practice for the detection of structural breaks. We apply the proposed model selection procedure to characterize structural breaks in the parameters of an autoregressive model for post-war U.S. inflation. We find important changes in both residual variance and conditional mean parameters, the latter of which is revealed only upon conducting the joint model selection procedure developed here.

Keywords: Posterior Model Probability, Markov Chain Monte Carlo, Inflation Persistence

JEL Classification: C11, C22, C52, E31

Suggested Citation

Levin, Andrew and Piger, Jeremy M., Bayesian Model Selection for Structural Break Models. Available at SSRN: https://ssrn.com/abstract=1132463 or http://dx.doi.org/10.2139/ssrn.1132463

Andrew Levin

affiliation not provided to SSRN

Jeremy M. Piger (Contact Author)

University of Oregon - Department of Economics ( email )

Eugene, OR 97403
United States

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