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No. 658: Multivariate Methods for Monitoring Structural Change

Jan J.J. Groen , Federal Reserve Bank of New York
George Kapetanios , Queen Mary, University of London
Simon Price , Bank of England and City University

February 1, 2010

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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

J.E.L classification codes: C100, C590

Keywords:Monitoring, Structural change, Panel, CUSUM, Fluctuation test

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