Yin Lu , Chunbai Tao , Di Wang , Gazi Salah Uddin , Libo Wu , Xuening Zhu
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引用次数: 0
Abstract
Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. In this work, we introduce two dynamic spatial autoregression (DSAR) models aimed at capturing temporal trends and depicting the asymmetric network effects of the units. For both DSAR models, we propose a truncated Yule–Walker estimation method, which is tailored to achieve robust estimation in the presence of heavy-tailed data. Additionally, we extend this robust estimation procedure to a constrained estimation framework using the Dantzig selector, enabling the identification of sparse network effects observed in real-world applications. Theoretically, the minimax optimality of proposed estimators is derived under certain conditions on the weighting matrix. Empirical studies, including an analysis of financial contagion in the Chinese stock market and the dynamics of live streaming popularity, demonstrate the practical efficacy of our methods.
期刊介绍:
The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.