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Structural Change and Time Series Analysis

  • Lyle D. Broemeling

Summary

This investigation introduces changing-parameter ARMA processes as a way to model a time series. Many time series exhibit a changing trend or a changing autocorrelation structure; that is to say, they have certain nonstationary characteristics that cannot be modeled by the usual ARMA representation. The analysis of a changing parameter process is accomplished by a Bayesian approach, where the posterior distributions of the parameters are derived, and the analysis is illustrated with a moving average model that has a changing autocorrelation function.

Keywords

Posterior Distribution Bayesian Analysis Time Series Analysis Shift Point Auto Covariance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden-Day.Google Scholar
  2. Broemeling, L.D. and Shaarawy, S. (1986), A Bayesian analysis of time series, Chapter 21 in: P. Goel and A. Zellner (eds.), Bayesian Inference and Decision Techniques, Amsterdam and New York: Elsevier.Google Scholar
  3. Broemeling, L.D. and Tsurumi, H. (1986), Econometrics and Structural Change. New York: Marcel Dekker.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Lyle D. Broemeling

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