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Strong Rules for Detecting the Number of Breaks in a Time Series

Listed author(s):
  • Filippo Altissimo

    (Bank of Italy)

  • Valentina Corradi

    (Queen Mary and Westfield College)

This paper proposes a new approach for detecting the number of structural breaks in a time series when estimation of the breaks is performed one at the time. We consider the case of shifts in the mean of a possibly nonlinear process, allowing for dependent and heterogeneous observations. This is accomplished through a simple, sequential, almost sure rule ensuring that, in large samples, both the probabilities of overestimating and underestimating the number of breaks are zero. A new estimator for the long run variance which is consistent also in the presence of neglected breaks is proposed. The finite sample behavior is investigated via a simulation exercise. The sequential procedure, applied to the weekly Eurodollar interest rate, detects multiple breaks over the period 1973-1995.

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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0574.

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Date of creation: 01 Aug 2000
Handle: RePEc:ecm:wc2000:0574
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  24. Rudebusch, Glenn D., 1995. "Federal Reserve interest rate targeting, rational expectations, and the term structure," Journal of Monetary Economics, Elsevier, vol. 35(2), pages 245-274, April.
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  29. Vogelsang, Timothy J, 1998. "Testing for a Shift in Mean without Having to Estimate Serial-Correlation Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 73-80, January.
  30. de Jong, Robert M., 2000. "A Strong Consistency Proof For Heteroskedasticity And Autocorrelation Consistent Covariance Matrix Estimators," Econometric Theory, Cambridge University Press, vol. 16(02), pages 262-268, April.
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