Upcoming Conferences

"Structural Break Detection for Nonlinear Time Series"

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 to 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 nonlinear time series data that either exhibit structural breaks or can

be well approximated by piecewise stationary time series. 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 locally stationary models that are smooth, we will also consider

extensions to piece-wise nonlinear models including stochastic volatility

and GARCH models.

(This research is joint work with Thomas Lee and Gabriel Rodriguez-Yam.)