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Change point analyses are concerned with identifying positions of an orderedstochastic process that undergo abrupt local changes of some underlyingdistribution. When multiple processes are observed, it is often the case thatinformation regarding the change point positions is shared across the differentprocesses. This work describes a method that takes advantage of this type ofinformation. Since the number and position of change points can be describedthrough a partition with contiguous clusters, our approach develops a jointmodel for these types of partitions. We describe computational strategiesassociated with our approach and illustrate improved performance in detectingchange points through a small simulation study. We then apply our method to afinancial data set of emerging markets in Latin America and highlightinteresting insights discovered due to the correlation between change pointlocations among these economies.

Using Joint Random Partition Models for Flexible Change Point Analysis in Multivariate Processes