Prior elicitation in multiple change-point models

Gary Koop, Simon M. Potter

Research output: Working paper

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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.
Original languageEnglish
Place of PublicationLeicester
Number of pages29
Publication statusPublished - Sep 2004

Publication series

NameDepartment of Economics Working Papers
PublisherUniversity of Leicester
Volume04/26

Keywords

  • bayesian inference
  • change-point models
  • change-points
  • statistics
  • economics

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