Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors

Mark Fisher and Mark J. Jensen

Working Paper 2018-2
February 2018

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Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes toward the value of the average regime. To overcome this shrinkage, we model the hierarchical prior nonparametrically by letting the hyperparameter's prior—in other words, the hyperprior—be unknown and modeling it with a Dirichlet processes prior. To apply a nonparametric hierarchical prior to the probability of a break occurring, we extend the change point model to a multiple-change-point panel model. The hierarchical prior then shares in the cross-sectional information of the break processes to estimate the transition probabilities. We apply our multiple-change-point panel model to a longitudinal data set of actively managed, U.S. equity, mutual fund returns to measure fund performance and investigate the chances of a skilled fund being skilled in the future.

JEL classification: C11, C14, C41, G11, G17

Key words: Bayesian nonparametric analysis, change points, Dirichlet process, hierarchical priors, mutual fund performance

The authors thank Vikas Agarwal, Sylvia Frühwirth-Schnatter, and Paula Tkac for helping to make this paper better. They also thank the conference participants at the 2015 Bayesian Workshop of the Rimini Centre of Economic Analysis in Rimini, Italy; the 2015 European Seminar on Bayesian Econometrics in Gerzensee, Switzerland; the 2015 Conference on Computational and Financial Econometrics in London; the 2016 All Georgia Finance Conference in Atlanta, Georgia; the 2017 XVII ESTE Brazilian School of Time Series and Econometrics in São Carlos, Brazil; the department members of the Vienna University of Economics and Business, where one of the authors visited; McMasters University; the University of Montreal; Clemson University; and the University of North Carolina-Charlotte for their helpful comments and suggestions. The views expressed here are the authors' and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Mark Fisher, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, 404-498-8757,, or Mark J. Jensen, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, 404-498-8019,
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