Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression

Brian Hanlon and Catherine Forbes

No 8/02, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The principle that the simplest model capable of describing observed phenomena should also correspond to the best description has long been a guiding rule of inference. In this paper a Bayesian approach to formally implementing this principle is employed to develop model selection criteria for detecting structural change in financial and economic time series. Model selection criteria which allow for multiple structural breaks and which seek the optimal model order and parameter choices within regimes are derived. Comparative simulations against other popular information based model selection criteria are performed. Application of the derived criteria are also made to example financial and economic time series.

Keywords: Complexity theory; segmentation; break points; change points; model selection; model choice. (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2002-08
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2002/wp8-02.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2002-8

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().