报告人： Dan Yang（University of Hong Kong）
时间：2025-11-20 15:10-17:00
地点：Room 217, Guanghua Building 2
Abstract:
Jointly modeling and forecasting economic and financial variables across a large set of countries has long been a significant challenge. Two primary approaches have been utilized to address this issue: the vector autoregressive model with exogenous variables (VARX) and the matrix autoregression (MAR). The VARX model captures domestic dependencies but treats variables exogenous to represent global factors driven by international trade. In contrast, the MAR model simultaneously considers variables from multiple countries but ignores the trade network. In this paper, we propose an extension of the MAR model that achieves these two aims at once, i.e., studying both international dependencies and the impact of the trade network on the global economy. Additionally, we introduce a sparse component to the model to differentiate between systematic and idiosyncratic cross-predictability. To estimate the model parameters, we propose both a likelihood estimation method and a bias-corrected alternating minimization version. We provide theoretical and empirical analyses of the model's properties, alongside presenting intriguing economic insights derived from our findings.
About the Speaker:
Dan Yang is an Associate Professor in Innovation and Information Management of the Business School and Associate Director of Institute of Digital Economy and Innovation at the University of Hong Kong. She received her doctoral degree in Statistics from the Wharton School of the University of Pennsylvania and her bachelor’s degrees in Statistics and Economics from Peking University. Prior to joining HKU, she was an Assistant Professor in Department of Statistics at Rutgers University.
Professor Yang’s research interests include tensor data, high-dimensional statistical inference, time series, dimension reduction, network data, functional data, and business applications in economics, finance and healthcare. Her work has been published in journals such as the Journal of the American Statistical Association (with discussion), Annals of Statistics, Journal of the Royal Statistical Society Series B, Journal of Econometrics, Journal of Machine Learning Research, among others. She also served as an associate editor for Statistica Sinica.
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