  This paper considers the prominent problem of change-point detection in
regression. The study suggests a novel testing procedure featuring a fully
data-driven calibration scheme. The method is essentially a black box,
requiring no tuning from the practitioner. The approach is investigated from
both theoretical and practical points of view. The theoretical study
demonstrates proper control of first-type error rate under $H_0$ and power
approaching $1$ under $H_1$. The experiments conducted on synthetic data fully
support the theoretical claims. In conclusion, the method is applied to
financial data, where it detects sensible change-points. Techniques for
change-point localization are also suggested and investigated.
