Strong rules for detecting the number of breaks in a time series

Filippo Altissimo, Valentina Corradi

Research output: Contribution to journalArticlepeer-review

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. A tendency to overreject the null hypothesis emerges for sample of moderate size, and so we suggest a small sample correction. The sequential procedure, applied to the weekly Eurodollar interest rate, detects multiple breaks over the period 1973-1995.

Original languageEnglish (US)
Pages (from-to)207-244
Number of pages38
JournalJournal of Econometrics
Volume117
Issue number2
DOIs
StatePublished - Dec 2003

Keywords

  • Brownian bridge
  • Law of the iterated logarithm
  • Multiple structural breaks
  • Sequential hypothesis testing

ASJC Scopus subject areas

  • Economics and Econometrics

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