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

Listed author(s):
  • Altissimo, F.
  • Corradi, V.
Registered author(s):

    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 Exeter University, Department of Economics in its series Discussion Papers with number 0011.

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    Length: 43 pages
    Date of creation: 2000
    Handle: RePEc:exe:wpaper:0011
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