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

Filippo Altissimo and V. Corradi

Discussion Papers from University of Exeter, Department of Economics

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.

Keywords: TESTS; TIME SERIES; BEHAVIOUR (search for similar items in EconPapers)
JEL-codes: C20 C22 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2000
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Journal Article: Strong rules for detecting the number of breaks in a time series (2003)
Working Paper: Strong Rules for Detecting the Number of Breaks in a Time Series (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:exe:wpaper:0011

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