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If multiple series co\u2010break 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\u2010by\u2010case 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\u2010breaking series. This is robust to a cross\u2010sectional 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 \u00a9 2011 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.1272","type":"journal-article","created":{"date-parts":[[2011,10,10]],"date-time":"2011-10-10T16:37:23Z","timestamp":1318264643000},"page":"250-274","source":"Crossref","is-referenced-by-count":30,"title":["MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE"],"prefix":"10.1002","volume":"28","author":[{"given":"Jan J. 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