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Multivariate Methods for Monitoring Structural Change

Author

Listed:
  • Jan J.J. Groen

    (Federal Reserve Bank of New York)

  • George Kapetanios

    (Queen Mary, University of London)

  • Simon Price

    (Bank of England and City University)

Abstract

Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices

Suggested Citation

  • Jan J.J. Groen & George Kapetanios & Simon Price, 2010. "Multivariate Methods for Monitoring Structural Change," Working Papers 658, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:658
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    2. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    3. Eklund, Jana & Kapetanios, George & Price, Simon, 2010. "Forecasting in the presence of recent structural change," Bank of England working papers 406, Bank of England.
    4. Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019. "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1596-1612.
    5. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    6. 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.
    7. S. O. Tickle & I. A. Eckley & P. Fearnhead, 2021. "A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1303-1325, October.
    8. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    9. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    10. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    11. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.

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    More about this item

    Keywords

    Monitoring; Structural change; Panel; CUSUM; Fluctuation test;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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