Time-Varying Model Averaging of Multi-layer Network Vector Autoregressions

Degui Li (), Yuying Sun () and Boyao Wu ()
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Degui Li: Faculty of Business Administration, University of Macau
Yuying Sun: Chinese Academy of Sciences
Boyao Wu: University of International Business and Economics

No 202640, Working Papers from University of Macau, Faculty of Business Administration

Abstract: In this paper, we introduce a flexible time-varying multi-layer network vector autoregression (VAR) model framework for large-scale time series, allowing agents in dynamic systems to interact through multiple channels and incorporating multiple adjacency matrices to capture network spillover effects. We propose a penalized model averaging method to determine a time-varying optimal combination of multi-layer network VAR candidate models whose number may be divergent. Under some regularity conditions, the asymptotic properties such as asymptotic optimality and convergence rates of the proposed time-varying weight estimation are derived in the contexts of both the in-sample fitting and out-of-sample prediction. In addition, we extend the conformal prediction method to construct prediction bands for locally stationary time series. Monte-Carlo simulation studies and an empirical application to forecast CPI inflation by combining multiple network information are given to illustrate reliable finite-sample estimation and predictive performance of the developed methodology.

Keywords: asymptotic optimality; conformal prediction; model averaging; multi-layer network; time-varying VAR (search for similar items in EconPapers)
JEL-codes: C32 C38 C55 C58 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2026-06
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Published in UM-FBA Working Paper Series

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