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Multivariate Methods For Monitoring Structural Change

  • Jan J. J. Groen
  • George Kapetanios
  • Simon Price

Detection of structural change is a critical empirical activity, but continuous 'monitoring' of time series for structural changes in real time raises well-known econometric issues. These have been explored in a univariate context. If multiple series co-break, as may be plausible, then it is possible that simultaneous examination of a multivariate set of data would help 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 a maximum CUSUM detection test. Monte Carlo experiments suggest that there is 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. The method is applied to UK RPI inflation in the period after 2001. A break is detected which would not have been picked up by univariate methods.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 28 (2013)
Issue (Month): 2 (03)
Pages: 250-274

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Handle: RePEc:wly:japmet:v:28:y:2013:i:2:p:250-274
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  16. repec:cup:cbooks:9780521634809 is not listed on IDEAS
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