"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Estimation of
Structural Breaks in Nonstationary Time Series

Stacey Hancock , Ph.D. Candidate, Department of Statistics, Colorado State University

Thursday, July 24, 2008

1 p.m. 006 Statistics


Many time series exhibit structural breaks in a variety of ways, the
most obvious being a mean level shift. In this case, the mean level of
the process is constant over periods of time, jumping to different
levels at times called change-points. These jumps may be due to
outside influences such as changes in government policy or
manufacturing regulations. Structural breaks may also be a result of
changes in variability or changes in the spectrum of the process. The
goal of this research is to estimate where these structural breaks
occur and to provide a model for the data within each stationary
segment. The program Auto-PARM (Automatic Piecewise AutoRegressive
Modeling procedure), developed by Davis, Lee, and Rodriguez-Yam
(2006), uses the minimum description length principle to estimate the
number and locations of change-points in a time series by fitting
autoregressive models to each segment. This research shows that when
the true underlying model is segmented autoregressive, the estimates
obtained by Auto-PARM are consistent. Under a more general time series
model exhibiting structural breaks, Auto-PARM's estimates of the
number and locations of change-points are again consistent, and the
segmented autoregressive model provides a useful approximation to the
true process.

Advisory Committee

Richard A. Davis, Adviser
Hari Iyer, Co-Adviser
Peter Brockwell, Committee Member
N. Thompson Hobbs (Natural Resource Ecology Lab)