Location of Repository

Prior Elicitation in Multiple Change-point Models

By Gary Koop and Simon M. Potter

Abstract

This paper discusses Bayesian inference in change-point models. Existing approaches involve placing a (possibly hierarchical) prior over a known number of change-points. We show how two popular priors have some potentially undesirable properties (e.g. allocating excessive prior weight to change-points near the end of the sample) and discuss how these properties relate to imposing a fixed number of changepoints in-sample. We develop a new hierarchical approach which allows some of of change-points to occur out-of sample. We show that this prior has desirable properties and handles the case where the number of change-points is unknown. Our hierarchical approach can be shown to nest a wide variety of change-point models, from timevarying parameter models to those with few (or no) breaks. Since our prior is hierarchical, data-based learning about the parameter which controls this variety occurs

Publisher: Dept. of Economics, University of Leicester
Year: 2004
OAI identifier: oai:lra.le.ac.uk:2381/4755

Suggested articles

Preview

Citations

  1. (1993). A Bayesian analysis for change point problems, doi
  2. (1979). A note on the interval between coal mining disasters, doi
  3. (2001). Are apparent findings of nonlinearity due to structural instability in economic time series?, doi
  4. (1994). Bayesian retrospective multiple-changepoint identification, doi
  5. (1996). Calculating posterior distributions and modal estimates in Markov mixture models, doi
  6. (1998). Estimating and testing linear models with multiple structural changes, doi
  7. (1964). Estimating the current mean of a Normal distribution which is subject to changes in time, doi
  8. (1998). Estimation and comparison of multiple change-point models, doi
  9. (2001). Evolving post-World War II inflation dynamics, doi
  10. (2004). Forecasting and estimating multiple changepoint models with an unknown number of change-points, doi
  11. (1999). Forecasting Non-stationary Economic Time Series. doi
  12. (2004). Forecasting time series subject to multiple structural breaks, manuscript available at http://www.econ.cam.ac.uk/faculty/pesaran/HMCMultiBreakJune04.pdf.
  13. (1999). Has the US economy become more stable? A Bayesian approach based on a Markov switching model of the business cycle, doi
  14. (1992). Hierarchical Bayesian analysis of changepoint problems, doi
  15. (2004). Learning, forecasting and structural breaks, manuscript available at http://www.nd.edu/~meg/MEG2004/GordonStephen.pdf .
  16. (1995). Marginal likelihood from the Gibbs output, doi
  17. (2003). Optimally testing general breaking processes in linear time series models, manuscript available at http://www.econ.ucsd.edu/~gelliott/RECENTPAPERS.htm.
  18. (2000). Output fluctuations in the United States: What has changed since the early 1980s? doi
  19. (2001). The equity premium and structural breaks, doi
  20. (2003). The less volatile U.S. economy: A Bayesian investigation of timing, breadth, and potential explanations, working paper 2001-016C, The Federal Reserve Bank of St.
  21. (1988). What is the likelihood Function? doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.