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MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE

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  • The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England, the Federal Reserve Bank of New York or the Federal Reserve System.

Correspondence to: George Kapetanios, School of Economics and Finance, Queen Mary, University of London, Mile End Road, London E1 4NS UK. E-mail: g.kapetanios@qmul.ac.uk

SUMMARY

Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ for 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. Copyright © 2011 John Wiley & Sons, Ltd.

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