Detecting contemporaneous mean co-breaking via ART and PCA

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 called ART to detect the presence of breaks and Principal Component Analysis to generate 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.