JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1065 10.6339/22-JDS1065 Statistical Data Science Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow ChenElynn Y.elynn.chen@stern.nyu.edu1∗ ChenRong2 New York University, Stern School of Business, United States Rutgers University, Department of Statistics, United States Corresponding author. Email: elynn.chen@stern.nyu.edu. 20235122022213490507 Supplementary Material

Supplementary material (Chen and Chen, 2022) contains exploratory analysis of the international trade data used in the present paper and asymmetric export and import analysis from applying Model (2) to the international trade volume data.

6420221192022 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

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.

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