Structural Break Detection in Time Series Models
by
Richard A. Davis
Department of Statistics, Colorado State University
Coauthors: Thomas Lee and Gabriel Rodriguez-Yam

Much of the recent interest in time series modeling has focused on data from financial markets, from communications channels, from speech recognition and from engineering applications, where the need for non-Gaussian, non-linear, and nonstationary models is clear. With faster computation and new estimation algorithms, it is now possible make significant in-roads on modeling more complex-phenomena. In this talk, we will develop estimation procedures for a class of models that can be used for analyzing a wide range of time series data that exhibit structural breaks. The novelty of the approach taken here is to combine the use of genetic algorithms with the principle of minimum description length (MDL), an idea developed by Rissanan in the 1980s, to find "optimal" models over a potentially large class of models. This methodology will be demonstrated in a number of applications. In addition to fitting piece-wise autoregressive models, which works well even for local stationary models that are smooth, we will also consider extensions to piece-wise nonlinear models including stochastic volatility and GARCH models.

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Date received: September 13, 2005

Copyright © 2005 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caqt-69.