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

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  • Altissimo, F.
  • Corradi, V.

Abstract

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.

Suggested Citation

  • Altissimo, F. & Corradi, V., 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Discussion Papers 0011, University of Exeter, Department of Economics.
    • Handle: RePEc:exe:wpaper:0011
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    TESTS ; TIME SERIES ; BEHAVIOUR;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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