The partition problem: case studies in Bayesian screening for time-varying model structure
Abstract
This paper presents two case studies of data sets where the main inferential goal is to characterize time-varying patterns in model structure. Both of these examples are seen to be general cases of the so-called "partition problem," where auxiliary information (in this case, time) defines a partition over sample space, and where different models hold for each element of the partition. In the first case study, we identify time-varying graphical structure in the covariance matrix of asset returns from major European equity indices from 2006--2010. This structure has important implications for quantifying the notion of financial contagion, a term often mentioned in the context of the European sovereign debt crisis of this period. In the second case study, we screen a large database of historical corporate performance in order to identify specific firms with impressively good (or bad) streaks of performance.
Cite
@article{arxiv.1111.0617,
title = {The partition problem: case studies in Bayesian screening for time-varying model structure},
author = {Zesong Liu and Jesse Windle and James G. Scott},
journal= {arXiv preprint arXiv:1111.0617},
year = {2011}
}