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Estimating the Locations of Multiple Change Points in the Mean

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  • Joe H. Sullivan

    (Mississippi State University, MSU)

Abstract

Summary Detecting and estimating the number and locations of multiple change points is difficult. Sometimes a single-change method can locate multiple shifts by recursively dividing the data at the most likely location of a single shift. However, a single-change method may not detect the presence of multiple changes and may not accurately estimate a division point when multiple changes are present. The Schwarz information criterion offers a direct way to estimate the number of shifts, but the locations maximizing the likelihood function must be known for each possible number of shifts. The latter task is computationally infeasible for a realistic amount of data. This paper proposes a general algorithm for estimating the likelihood-maximizing locations and gives an example in which multiple changes are detected. The performance is evaluated using simulation and the proposed method is shown to be superior to the recursive application of a single shift procedure.

Suggested Citation

  • Joe H. Sullivan, 2002. "Estimating the Locations of Multiple Change Points in the Mean," Computational Statistics, Springer, vol. 17(2), pages 289-296, July.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200107
    DOI: 10.1007/s001800200107
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    References listed on IDEAS

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    1. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
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    Cited by:

    1. Bill Russell & Dooruj Rambaccussing, 2016. "Breaks and the Statistical Process of Inflation: The Case of the ‘Modern’ Phillips Curve," Dundee Discussion Papers in Economics 294, Economic Studies, University of Dundee.
    2. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    3. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    4. Addona Vittorio & Yates Philip A, 2010. "A Closer Look at the Relative Age Effect in the National Hockey League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-19, October.
    5. Bill Russell & Dooruj Rambaccussing, 2019. "Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve," Empirical Economics, Springer, vol. 56(5), pages 1455-1475, May.

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    Keywords

    Schwarz Information Criterion;

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