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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:wp658
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    File URL: http://www.econ.qmul.ac.uk/media/econ/research/workingpapers/archive/wp658.pdf
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    References listed on IDEAS

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    1. Achim Zeileis & Kurt Hornik, 2007. "Generalized M-fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508.
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    Cited by:

    1. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    2. 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.
    3. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    4. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2018.
    5. KUROZUMI, Eiji, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    6. repec:bla:jtsera:v:38:y:2017:i:5:p:791-805 is not listed on IDEAS

    More about this item

    Keywords

    Monitoring; Structural change; Panel; CUSUM; Fluctuation test;

    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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