Jan Groen
George Kapetanios
Simon Price

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.

Data and Resources

Suggested Citation

Groen, Jan; Kapetanios, George; Price, Simon (2013): MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022320.0731302606