Factor Network Autoregressions

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.

针对具有复杂网络结构的时间序列，我们提出了因子网络自回归（Factor Network Autoregressive, FNAR）模型。该模型的系数可反映经济主体间多种不同类型的关联（即“多层网络”），我们通过一种新颖的基于张量的主成分方法，将这些关联凝练为数量更少的网络矩阵（即“网络因子”）。当网络层数、节点数与时间点数量均趋于无穷时，我们推导得到了该模型因子、因子载荷以及FNAR模型系数估计量的一致性与渐近正态性理论结果。我们的方法结合了两种不同的维度缩减技术，可应用于高维数据集。模拟实验结果表明，我们提出的估计量在有限样本情境下表现优异。在实证应用部分，我们基于多种国际贸易与金融关联渠道，运用FNAR模型探究了各国GDP增长率之间的跨国相互依存关系。该模型不仅能够对宏观经济网络效应进行充分刻画，还可实现对GDP增长率的高精度预测。

搜集汇总

背景与挑战

背景概述

该数据集提出了因子网络自回归（FNAR）模型，用于分析具有复杂网络结构的时间序列数据，并通过实证应用验证了模型在宏观经济网络效应分析和GDP增长率预测中的有效性。