  Change point analyses are concerned with identifying positions of an ordered
stochastic process that undergo abrupt local changes of some underlying
distribution. When multiple processes are observed, it is often the case that
information regarding the change point positions is shared across the different
processes. This work describes a method that takes advantage of this type of
information. Since the number and position of change points can be described
through a partition with contiguous clusters, our approach develops a joint
model for these types of partitions. We describe computational strategies
associated with our approach and illustrate improved performance in detecting
change points through a small simulation study. We then apply our method to a
financial data set of emerging markets in Latin America and highlight
interesting insights discovered due to the correlation between change point
locations among these economies.
