融合贸易网络与全局矩阵自回归，揭示跨国经济变量的系统性关联
Sparsity-Induced Global Matrix Autoregressive Model with Auxiliary Network Data
- 构建兼顾国家间依赖与贸易网络影响的矩阵自回归模型
- 引入稀疏结构区分系统性与个体化预测关系，提升解释力
- 适用于宏观经济建模与全球金融风险分析的研究者
同时建模和预测多个国家的经济与金融变量长期以来是一个重大挑战。主流方法包括带外生变量的向量自回归（VARX）和矩阵自回归（MAR）。VARX捕捉国内依赖关系，但将变量视为外生以表征由国际贸易驱动的全球因素；而MAR虽能同时处理多国变量，却忽略了贸易网络的影响。本文提出一种扩展的MAR模型，同时实现对国际依赖与贸易网络影响的建模。此外，模型引入稀疏成分以区分系统性与非系统性跨预测关系。我们提出了似然估计法及偏差校正交替最小化算法用于参数估计，并提供了理论与实证分析，揭示了具有启发性的经济洞见。
原文摘要 · Abstract (English)
Jointly modeling and forecasting economic and financial variables across a large set of countries has long been a significant challenge. Two primary approaches have been utilized to address this issue: the vector autoregressive model with exogenous variables (VARX) and the matrix autoregression (MAR). The VARX model captures domestic dependencies, but treats variables exogenous to represent global factors driven by international trade. In contrast, the MAR model simultaneously considers variables from multiple countries but ignores the trade network. In this paper, we propose an extension of the MAR model that achieves these two aims at once, i.e., studying both international dependencies and the impact of the trade network on the global economy. Additionally, we introduce a sparse component to the model to differentiate between systematic and idiosyncratic cross-predictability. To estimate the model parameters, we propose both a likelihood estimation method and a bias-corrected alternating minimization version. We provide theoretical and empirical analyses of the model's properties, alongside presenting intriguing economic insights derived from our findings.
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