  International trade research plays an important role to inform trade policy
and 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 internationally
comparable trading data among a large number of countries over time, providing
a goldmine for empirical analysis of international trade. Meanwhile, an array
of new statistical methods are recently developed for dynamic network analysis.
However, these advanced methods have not been utilized for analyzing such
massive dynamic cross-country trading data. International trade data can be
viewed as a dynamic transport network because it emphasizes the amount of goods
moving across a network. Most literature on dynamic network analysis
concentrates on the connectivity network that focuses on link formation or
deformation rather than the transport moving across the network. We take a
different perspective from the pervasive node-and-edge level modeling: the
dynamic transport network is modeled as a time series of relational matrices.
We adopt a matrix factor model of \cite{wang2018factor}, with 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. The proposed method is able to unveil the latent dynamic
structure and achieve the objective of dimension reduction. We applied the
proposed framework and methodology to a data set 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.

Econometrics;Methodology