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A pairwise likelihood-based approach for changepoint detection in multivariate time series models

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  • Ting Fung Ma
  • Chun Yip Yau

Abstract

This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.

Suggested Citation

  • Ting Fung Ma & Chun Yip Yau, 2016. "A pairwise likelihood-based approach for changepoint detection in multivariate time series models," Biometrika, Biometrika Trust, vol. 103(2), pages 409-421.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:2:p:409-421.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw002
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    References listed on IDEAS

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    1. de Bondt, Gabe J. & Dieden, Heinz C. & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders," Economic Modelling, Elsevier, vol. 41(C), pages 46-54.
    2. Ling, Shiqing, 2016. "Estimation Of Change-Points In Linear And Nonlinear Time Series Models," Econometric Theory, Cambridge University Press, vol. 32(2), pages 402-430, April.
    3. de Bondt, Gabe & Dieden, Heinz Christian & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders for the euro area," Statistics Paper Series 6, European Central Bank.
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    Cited by:

    1. Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
    2. Hajra Siddiqa & Sajid Ali & Ismail Shah, 2021. "Most recent changepoint detection in censored panel data," Computational Statistics, Springer, vol. 36(1), pages 515-540, March.
    3. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.

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