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Strong Rules for Detecting the Number of Breaks in a Time Series Author info | Abstract | Publisher info | Download info | Related research | Statistics Altissimo, F.
Corradi, V.
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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 University of Exeter, School of Business and Economics in its series Discussion Papers with number
00/11.
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Length: 43 pages
Date of creation: 2000Date of revision:
Handle: RePEc:fth:exetec:00/11Contact details of provider: Postal: School of Business and Economics University of Exeter Streatham Court Rennes Drive Exeter EX4 4PU Phone: (01392) 263218 Fax: (01392) 263242 Web page: http://www.exeter.ac.uk/sobe/ More information through EDIRC
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Keywords: TESTS ; TIME SERIES ; BEHAVIOUR ; Other versions of this item:
Find related papers by 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
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