Selective linear segmentation for detecting relevant parameter changes


讲座时间:2018年1128日 上午10:30 - 12:00


讲座嘉宾Arnaud DufaysUniversity of Namur



Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter changes when a change-point is detected. When the number of break points is small, an exhaustive search based on a consistent criterion is used to select the best set of parameters that changes over time. In the other situation, we use a penalized likelihood approach to reduce the number of models to consider and we prove that the penalty function will lead to a  consistent selection of the true model. Estimation in such a case is carried out via the deterministic annealing expectation-minimisation algorithm. Interestingly, the method accounts for model selection uncertainty and provides a probability of selecting a specific set of covariates. Monte carlo simulations highlight that the method works well in small and large samples for many different time series models. An application on hedge funds returns shows how we can exploit the framework.  


Dr. Dufays is an Assistant Professor in Economics from Department of Business Administration, University of Namur. He obtained his Ph.D. degree from University of Louvain. His research interest focuses on Bayesian financial econometrics and Bayesian methods.His work has been published in well-recognized international journals such as Journal of Econometrics, Journal of Business and Economic Statistics, Journal of Financial Econometrics, Journal of Empirical Finance, etc.