Estimation of Grouped Time-Varying Network Vector Autoregression Models

Degui Li, Bin Peng, Songqiao Tang and Weibiao Wu
Additional contact information
Bin Peng: Department of Econometrics and Business Statistics, Monash University in Australia
Songqiao Tang: School of Mathematical Sciences, Zhejiang University
Weibiao Wu: Department of Statistics, University of Chicago

No 202526, Working Papers from University of Macau, Faculty of Business Administration

Abstract: This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates which ignore the latent structure. We further modify the methodology and theory to allow for structural breaks in either the group membership, group number or group-specific coefficient functions. Numerical studies including Monte-Carlo simulation and an empirical application are presented to examine the finite-sample performance of the developed model and methodology.

Keywords: cluster analysis; network VAR; latent groups; local linear estimator; time-varying coefficients (search for similar items in EconPapers)
JEL-codes: C14 C32 C55 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2025-03
New Economics Papers: this item is included in nep-ets and nep-net
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Published in UM-FBA Working Paper Series

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Related works:
Working Paper: Estimation of Grouped Time-Varying Network Vector Autoregression Models (2024)
Working Paper: Estimation of Grouped Time-Varying Network Vector Autoregression Models (2024)
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