  Structural change detection problems are often encountered in analytics and
econometrics, where the performance of a model can be significantly affected by
unforeseen changes in the underlying relationships. Although these problems
have a comparatively long history in statistics, the number of studies done in
the context of multivariate data under nonparametric settings is still small.
In this paper, we propose a consistent method for detecting multiple structural
changes in a system of related regressions over a large dimensional variable
space. In most applications, practitioners also do not have a priori
information on the relevance of different variables, and therefore, both
locations of structural changes as well as the corresponding sparse regression
coefficients need to be estimated simultaneously. The method combines
nonparametric energy distance minimization principle with penalized regression
techniques. After showing asymptotic consistency of the model, we compare the
proposed approach with competing methods in a simulation study. As an example
of a large scale application, we consider structural change point detection in
the context of news analytics during the recent financial crisis period.
