Factor Network Autoregressions

Bibliographic Data

ID19419745
AuthorsMatteo Barigozzi (0000-0001-9090-5708, University of Bologna, corresponding author), Giuseppe Cavaliere (0000-0002-2856-0005, University of Bologna), Graziano Moramarco (0000-0003-4476-4267, University of Bologna)
Year2025
Volume43
Issue4
Pages1105-1118
Publication date2025-10-02
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2025.2476695
OpenAlexW4408337937
LanguageEN
Citations received1
References cited29

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and the coefficients of the FNAR, as the number of layers, nodes and time points diverges to infinity. Our approach combines two different dimension-reduction techniques and can be applied to high-dimensional datasets. Simulation results show the goodness of our estimators in finite samples. In an empirical application, we use the FNAR to investigate the cross-country interdependence of GDP growth rates based on a variety of international trade and financial linkages. The model provides a rich characterization of macroeconomic network effects as well as good forecasts of GDP growth rates

Econometrics · Economics · Programming language · Complex Network Analysis Techniques · Computer Science

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