Elynn Chen
Assistant Professor of Technology, Operations, and Statistics
Leonard N. Stern School of Business
New York University
Community Network Auto-Regression for High-Dimensional Time Series
Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector autoregressive (CNAR) model, which utilizes the network structure to characterize the dependence and intra-community homogeneity of the high dimensional time series. The CNAR model greatly increases the flexibility and generality of the network vector autoregressive (Zhu et al, 2017, NAR) model by allowing heterogeneous network effects across different network communities. In addition, the non-community-related latent factors are included to account for unknown cross-sectional dependence. The number of network communities can diverge as the network expands, which leads to estimating a diverging number of model parameters. We obtain a set of stationary conditions and develop an efficient two-step weighted least-squares estimator. The consistency and asymptotic normality properties of the estimators are established. The theoretical results show that the two-step estimator improves the one-step estimator by an order of magnitude when the error admits a factor structure. The advantages of the CNAR model are further illustrated on a variety of synthetic and real datasets. This is joint work with Jianqing Fan and Xuening Zhu.
Bio: Elynn Chen joined New York University Stern School of Business as an Assistant Professor of Technology, Operation and Statistics in September 2021. Professor Chen is generally interested in developing novel methodologies for data-driven decision-making and complex time series analysis, with applications in business, economics and health care. Her work has been applied to international trade, corporate finance, clinical dynamic treatments and has been recognized by NSF postdoctoral research award. Before joining NYU Stern, Professor Chen worked as a postdoctoral researcher at Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, Department of Operations Research and Financial Engineering at Princeton University, and Open AI. Professor Chen received her PhD in Statistics from Rutgers University, BA in Economics from Peking University and BS in Computer Science from Tsinghua University.
DATE: Wednesday, 4/12/23
TIME: 4:00 PM
PLACE: AUST 163
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mbd07b3d1410f18b9c1d73bf60934a998
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu