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

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  • Elynn Y. Chen
  • Rong Chen

Abstract

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.

Suggested Citation

  • Elynn Y. Chen & Rong Chen, 2019. "Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow," Papers 1901.00769, arXiv.org.
  • Handle: RePEc:arx:papers:1901.00769
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    References listed on IDEAS

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    1. Mark Huisman & Tom A. B. Snijders, 2003. "Statistical Analysis of Longitudinal Network Data With Changing Composition," Sociological Methods & Research, , vol. 32(2), pages 253-287, November.
    2. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    3. Donald R. Davis & David E. Weinstein, 2002. "What Role for Empirics in International Trade?," Aussenwirtschaft, University of St. Gallen, School of Economics and Political Science, Swiss Institute for International Economics and Applied Economics Research, vol. 57(04), pages 441-468, December.
    4. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    5. Sunanda Sen, 2010. "International Trade Theory and Policy: A Review of the Literature," Economics Working Paper Archive wp_635, Levy Economics Institute.
    6. Lam, Clifford & Yao, Qiwei, 2012. "Factor modeling for high-dimensional time series: inference for the number of factors," LSE Research Online Documents on Economics 45684, London School of Economics and Political Science, LSE Library.
    7. Hafner-Burton, Emilie M. & Kahler, Miles & Montgomery, Alexander H., 2009. "Network Analysis for International Relations," International Organization, Cambridge University Press, vol. 63(3), pages 559-592, July.
    8. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
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    Cited by:

    1. Yufeng Mao & Bin Peng & Mervyn Silvapulle & Param Silvapulle & Yanrong Yang, 2021. "Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model," Papers 2101.06805, arXiv.org.
    2. Chang, Jinyuan & Zhang, Henry & Yang, Lin & Yao, Qiwei, 2023. "Modelling matrix time series via a tensor CP-decomposition," LSE Research Online Documents on Economics 117644, London School of Economics and Political Science, LSE Library.
    3. Yuefeng Han & Rong Chen & Cun-Hui Zhang, 2020. "Rank Determination in Tensor Factor Model," Papers 2011.07131, arXiv.org, revised May 2022.
    4. Xialu Liu & Elynn Y. Chen, 2022. "Identification and estimation of threshold matrix‐variate factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1383-1417, September.
    5. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.
    6. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised Jul 2024.
    7. Zhao, Wandi & Gao, Yang, 2024. "Dynamic patterns and the latent community structure of sectoral volatility and jump risk contagion," Emerging Markets Review, Elsevier, vol. 59(C).
    8. Yufeng Mao & Bin Peng & Mervyn J Silvapulle & Param Silvapulle & Yanrong Yang, 2021. "Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model," Monash Econometrics and Business Statistics Working Papers 7/21, Monash University, Department of Econometrics and Business Statistics.

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