Journal
JOURNAL OF APPLIED ECONOMETRICS
Volume 28, Issue 2, Pages 250-274Publisher
WILEY
DOI: 10.1002/jae.1272
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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 (c) 2011 John Wiley & Sons, Ltd.
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