Multivariate Methods For Monitoring Structural Change
AbstractDetection 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 28 (2013)
Issue (Month): 2 (03)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
Other versions of this item:
- Jan J.J. Groen & George Kapetanios & Simon Price, 2010. "Multivariate Methods for Monitoring Structural Change," Working Papers 658, Queen Mary, University of London, School of Economics and Finance.
- Groen, Jan J J & Kapetanios, George & Price, Simon, 2009. "Multivariate methods for monitoring structural change," Bank of England working papers 369, Bank of England.
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
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