Dynamic Causal Networks with Multi-Scale Temporal Structure

• Activity Number: 432
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Graphics
• Author(s): Xinyu Kang* and Apratim Ganguly and Eric Kolaczyk
• Companies: Boston University and Boston University and Boston University
• Keywords: Causal Network ; Multi-scale Modeling ; Neighborhood Selection

Abstract

We will discuss a novel method to model multivariate time series using dynamic causal networks. This newly proposed method combines traditional multi-scale modeling and network based neighborhood selection, aiming at capturing the temporally local structure of the data while maintaining the sparsity of the potential interactions. Our multi-scale framework is based on recursive dyadic partitioning, which recursively partitions the temporal axis into finer intervals and allows us to detect local network structural changes at varying temporal resolutions. The dynamic neighborhood selection is achieved through penalized likelihood estimation, where the penalty seeks to limit the number of neighbors used to model the data. We present theoretical and numerical results describing the performance of our method, and comment on potential applications in financial economics and neuroscience.