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Robust estimation for dynamic spatial autoregression models with nearly optimal rates

Yin Lu, Chunbai Tao, Di Wang, Gazi Salah Uddin, Libo Wu and Xuening Zhu

Journal of Econometrics, 2025, vol. 251, issue C

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.

Keywords: Dynamic spatial autoregression; Network data; Robust estimation; Truncation method; Minimax optimality (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:251:y:2025:i:c:s0304407625001198

DOI: 10.1016/j.jeconom.2025.106065

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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