Multivariate Methods for Monitoring Structural Change

Posted: 11 Jun 2009

See all articles by Jan J. Groen

Jan J. Groen

Federal Reserve Bank of New York

George Kapetanios

King's College, London

Simon Price

Essex Business School; City University London - Department of Economics

Date Written: June 9, 2009

Abstract

Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ of time series for structural changes in real time raises well-known econometric issues. These have been explored in a univariate context. If multiple series co-break, as may be plausible, then it is possible that simultaneous examination of a multivariate set of data would help 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 a maximum CUSUM detection test. Monte Carlo experiments suggest that there is 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. The method is applied to UK RPI inflation in the period after 2001. A break is detected which would not have been picked up by univariate methods.

Keywords: monitoring, structural change, panel, CUSUM, fluctuation test

JEL Classification: C100, C590

Suggested Citation

Groen, Jan J. and Kapetanios, George and Price, Simon G., Multivariate Methods for Monitoring Structural Change (June 9, 2009). Available at SSRN: https://ssrn.com/abstract=1416825

Jan J. Groen

Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

HOME PAGE: http://nyfedeconomists.org/groen/

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Simon G. Price (Contact Author)

Essex Business School ( email )

Wivenhoe Park
Colchester, CO4 3SQ
United Kingdom

City University London - Department of Economics ( email )

Northampton Square
London, EC1V 0HB
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
750
PlumX Metrics