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|
|Location:||Room 106 Streatam Court|
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.