Abstract
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.
Citation
Groen, J. J. J., G. Kapetanios and S. Price (2013), “Multivariate Methods for Monitoring Structural Change” Journal of Applied Econometrics: Vol. 28, pages 250–274.