Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow
Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow
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DOI：
10.6339/22-jds1065
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Elynn Y. Chen;Rong Chen
中科院分区：
文献类型：
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作者：
Elynn Y. Chen;Rong Chen
国际贸易研究在为贸易政策提供信息和阐明更广泛的经济问题方面发挥着重要作用。随着信息技术的进步，经济机构发布了大量的国际可比贸易数据，为国际贸易的实证分析提供了金矿。国际贸易数据可以被视为动态运输网络，因为它强调跨网络边缘移动的货物数量。大多数有关动态网络分析的文献都集中于连接网络的参数化建模，重点关注链路的形成或变形，而不是跨网络的传输。我们从普遍的节点和边级建模中采取不同的非参数视角：动态传输网络被建模为关系矩阵的时间序列； Wang 等人的矩阵因子模型的变体。 （2019）被应用于为动态传输网络提供具体的解释。在该模型下，假设观察到的表面网络是由较低维度的潜在动态传输网络驱动的。我们的方法能够揭示潜在的动态结构并实现降维的目标。我们将所提出的方法应用于 1982 年至 2015 年 24 个国家（和地区）的月度贸易量数据集。我们的研究结果揭示了国际贸易的贸易中心、中心性、趋势和模式，并显示了与贸易政策相匹配的变化点。该数据集还为未来的国际贸易研究提供了肥沃的土壤。
International trade research plays an important role to inform trade policy and shed light on wider economic issues. With recent advances in information technology, economic agencies distribute an enormous amount of internationally comparable trading data, providing a gold mine for empirical analysis of international trade. International trading data can be viewed as a dynamic transport network because it emphasizes the amount of goods moving across network edges. Most literature on dynamic network analysis concentrates on parametric modeling of the connectivity network that focuses on link formation or deformation rather than the transport moving across the network. We take a different non-parametric perspective from the pervasive node-and-edge-level modeling: the dynamic transport network is modeled as a time series of relational matrices; variants of the matrix factor model of Wang et al. (2019) are applied to provide a specific interpretation for the dynamic transport network. Under the model, the observed surface network is assumed to be driven by a latent dynamic transport network with lower dimensions. Our method is able to unveil the latent dynamic structure and achieves the goal of dimension reduction. We applied the proposed method to a dataset of monthly trading volumes among 24 countries (and regions) from 1982 to 2015. Our findings shed light on trading hubs, centrality, trends, and patterns of international trade and show matching change points to trading policies. The dataset also provides a fertile ground for future research on international trade.