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Multivariate methods for monitoring structural change

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Author Info

  • Groen, Jan J J

    ()
    (Federal Reserve Bank of New York)

  • Kapetanios, George

    ()
    (Queen Mary and Westfield College)

  • Price, Simon

    ()
    (Bank of England)

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.

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Bibliographic Info

Paper provided by Bank of England in its series Bank of England working papers with number 369.

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Length: 38 pages
Date of creation: 08 Jun 2009
Date of revision:
Handle: RePEc:boe:boeewp:0369

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

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  1. George Kapetanios, 2007. "Dynamic factor extraction of cross-sectional dependence in panel unit root tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 313-338.
  2. Bai, Jushan & Lumsdaine, Robin L & Stock, James H, 1998. "Testing for and Dating Common Breaks in Multivariate Time Series," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 395-432, July.
  3. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  4. Kurt Hornik & Friedrich Leisch & Christian Kleiber & Achim Zeileis, 2005. "Monitoring structural change in dynamic econometric models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 99-121.
  5. Achim Zeileis & Kurt Hornik, 2007. "Generalized M-fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508.
  6. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037, Octomber.
  7. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  8. Kramer, Walter & Ploberger, Werner & Alt, Raimund, 1988. "Testing for Structural Change in Dynamic Models," Econometrica, Econometric Society, vol. 56(6), pages 1355-69, November.
  9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  10. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-65, September.
  11. Achim Zeileis, 2005. "A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 445-466.
  12. Hendry, David F., 2000. "On detectable and non-detectable structural change," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 45-65, July.
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Cited by:
  1. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.

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