Jan Groen
;
George Kapetanios
;
Simon Price
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multivariate methods for monitoring structural change (replication data)

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.

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Suggested Citation

Groen, Jan; Kapetanios, George; Price, Simon (2013): MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://journaldata.zbw.eu/dataset/multivariate-methods-for-monitoring-structural-change?activity_id=c035a870-20ca-4593-a872-f629b79f1ea2