Time-Varying Generalized Network Autoregressions

Boyao Wu, Jiti Gao and Deshui Yu

No 8/25, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We consider a general class of dynamic network autoregressions for high-dimensional time series with network dependence, extending existing dynamic models by allowing for timevarying model coefficients, cross-sectionally dependent errors and a general network structure smoothly evolving along the time. A nonparametric local linear kernel method is proposed to estimate these time-varying coefficients involved, and a recursive-design bootstrap procedure is developed to construct valid confidence intervals for time-varying coefficients in the presence of cross-sectional dependent errors. We establish asymptotic properties for the proposed local-linear based estimator and the bootstrap procedure under mild conditions. Both the proposed estimation and bootstrap procedures are illustrated using simulated and two real datasets. Our work contributes to high-dimensional time series associated with network effects and sheds light on bootstrap inference for locally stationary processes.

Keywords: cross-sectional dependence; dynamic network autoregression; high-dimensional time-series; MA(∞) representation (search for similar items in EconPapers)
JEL-codes: C14 C31 C33 D85 (search for similar items in EconPapers)
Pages: Â 37
Date: 2025
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