Abstract

http://ssrn.com/abstract=370488
 
 

References (30)



 
 

Citations (5)



 


 



Bayesian Methods for Change-Point Detection in Long-Range Dependent Processes


Bonnie K. Ray


IBM - T. J. Watson Research Center

Ruey S. Tsay


University of Chicago - Booth School of Business - Econometrics and Statistics


Journal of Time Series Analysis, Vol. 23, pp. 687-706, 2002

Abstract:     
We describe a Bayesian method for detecting structural changes in a long-range dependent process. In particular, we focus on changes in the long-range dependence parameter, d, and changes in the process level, p. Markov chain Monte Carlo (MCMC) methods are used to estimate the posterior probability and size of a change at time t, along with other model parameters. A time-dependent Kalman filter approach is used to evaluate the likelihood of the fractionally integrated ARMA model characterizing the long-range dependence. The method allows for multiple change points and can be extended to the long-memory stochastic volatility case. We apply the method to three examples, to investigate a change in persistence of the yearly Nile River minima, to investigate structural changes in the series of durations between intraday trades of IBM stock on the New York Stock Exchange, and to detect structural breaks in daily stock returns for the Coca Cola Company during the 1990s.

Number of Pages in PDF File: 19

Accepted Paper Series





Date posted: February 14, 2003  

Suggested Citation

Ray, Bonnie K. and Tsay, Ruey S., Bayesian Methods for Change-Point Detection in Long-Range Dependent Processes. Journal of Time Series Analysis, Vol. 23, pp. 687-706, 2002. Available at SSRN: http://ssrn.com/abstract=370488

Contact Information

Bonnie K. Ray (Contact Author)
IBM - T. J. Watson Research Center
Route 134
Kitchawan Road
Yorktown Heights, NY 10598
United States
Ruey S. Tsay
University of Chicago - Booth School of Business - Econometrics and Statistics ( email )
Chicago, IL 60637
United States
773-702-6750 (Phone)
773-702-4485 (Fax)
Feedback to SSRN


Paper statistics
Abstract Views: 1,195
Downloads: 10
References:  30
Citations:  5

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo5 in 0.266 seconds