Detecting contemporaneous mean co-breaking via ART

In this paper we propose a procedure to detect the presence of co-breaking i.e. of a common structural break occurring at an unknown date in a vector of time series. Co-breaking occurs if a linear combination of the time series cancels the break. Our procedure employs a regression tree based approach namely ART, to detect the presence of breaks in the mean of each one-dimensional component of a multidimensional time series and Principal Component Analysis to estimate the linear combinations of the vector series. On each of these linear combinations ART is performed again to detect the presence of a break. The combination that "hides" the co-breaking time is the one minimizing the employed splitting criterion. The results of a simulation study carried out to evaluate the performance of the proposed approach are presented and discussed.