Multivariate Methods for Monitoring Structural Change

Jan Groen, George Kapetanios and Simon Price

No 658, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural 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

Keywords: Monitoring; Structural change; Panel; CUSUM; Fluctuation test (search for similar items in EconPapers)
JEL-codes: C10 C59 (search for similar items in EconPapers)
Date: 2010-02-01
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Working Paper: Multivariate methods for monitoring structural change (2009)
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