Bayesian inference and prediction of a multiple-change-point panel model with nonparametric priors

Change point models using hierarchical priors have been very successful estimating the parameter values of short-lived regimes. However, hierarchical priors have been parametric which leads to shrinkage in the estimates of extraordinary regime parameters. We overcome this by modeling the hierarchical priors nonparametrically. We also extend the change point to a panel of change point processes where the prior shares in the probabilities of changing regimes. When applied to the returns from a panel of actively managed, US equity, mutual funds our multiple-change-point panel model finds mutual fund skill is not persistent but changes over time. (C) 2018 Elsevier B.V. All rights reserved.