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


Speaker:Valentina Corradi , Queen Mary and Westfield College
Date: Friday 25 February 2000
Time: 16:15
Location: Room 106 Streatam Court

Further details

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 a 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 is 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 weakly Eurodollar interest rate, detects multiple breaks over the period 1973-1995.