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International trade research plays an important role to inform trade policyand shed light on wider issues relating to poverty, development, migration,productivity, and economy. With recent advances in information technology,global and regional agencies distribute an enormous amount of internationallycomparable trading data among a large number of countries over time, providinga goldmine for empirical analysis of international trade. Meanwhile, an arrayof new statistical methods are recently developed for dynamic network analysis.However, these advanced methods have not been utilized for analyzing suchmassive dynamic cross-country trading data. International trade data can beviewed as a dynamic transport network because it emphasizes the amount of goodsmoving across a network. Most literature on dynamic network analysisconcentrates on the connectivity network that focuses on link formation ordeformation rather than the transport moving across the network. We take adifferent perspective from the pervasive node-and-edge level modeling: thedynamic transport network is modeled as a time series of relational matrices.We adopt a matrix factor model of \cite{wang2018factor}, with a specificinterpretation for the dynamic transport network. Under the model, the observedsurface network is assumed to be driven by a latent dynamic transport networkwith lower dimensions. The proposed method is able to unveil the latent dynamicstructure and achieve the objective of dimension reduction. We applied theproposed framework and methodology to a data set of monthly trading volumesamong 24 countries and regions from 1982 to 2015. Our findings shed light ontrading hubs, centrality, trends and patterns of international trade and showmatching change points to trading policies. The dataset also provides a fertileground for future research on international trade.

Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow