Working Papers 2002 – Abstracts
Model Selection Criteria for Segmented Time Series from a Bayesian Approach
to Information Compression
Brian Hanlon and Catherine Forbes
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.
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