Multivariate methods for monitoring structural change

Jan Groen, George Kapetanios and Simon Price

No 369, Bank of England working papers from Bank of England

Abstract: 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.

Keywords: monitoring; structural change; panel; CUSUM; fluctuation test (search for similar items in EconPapers)
JEL-codes: C10 C59 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2009-06-08
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE (2013)
Working Paper: Multivariate Methods for Monitoring Structural Change (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0369

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