Detecting multiple structural breaks in the mean via Atheoretical Regression Trees

This paper proposes a non parametric approach for dating structural breaks whose number and dates of occurrence are a priori unknown. In particular, the case of level shifts is considered. For the purpose of locating the break-dates the method exploits, in the framework of least square regression trees, the contiguity property introduced by Fisher for grouping a single real variable. The proposal is applied to the study of the mean water level of Michigan Huron lake also comparing the results to those of the well known procedure recently proposed by Bai and Perron.