arXiv 2 Jan 2019 · Econometrics · publishedJournal of Data Science (2022) · 28 citations (OpenAlex)
arXiv:1901.00769 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wang, D., Liu, X., and Chen, R (2018) Factor models for matrix-valued high-dimensional time series self | 1.000 | 5 | 4 | 100% |
| 2 | Xing, E. P., Fu, W., and Song, L (2010) A state-space mixed membership blockmodel for dynamic network tomography | 0.737 | 3 | 2 | 100% |
| 3 | Airoldi, E. M., Blei, D. M., Fienberg, S. E., and Xing, E. P (2008) Mixed membership stochastic blockmodels | 0.644 | 2 | 2 | 100% |
| 4 | Linnemann, H (1966) An econometric study of international trade flows, volume 234 | 0.585 | 3 | 1 | 100% |
| 5 | IMF (2017) Direction of Trade Statistics, International Monetary Fund | 0.405 | 1 | 1 | 100% |
| 6 | Chen, R., Xiao, H., and Yang, D (2018) Autoregressive models for matrix-valued time series self | 0.405 | 1 | 1 | 100% |
| 7 | Davis, D. R. and Weinstein, D. E (2001) What role for empirics in international trade? | 0.405 | 1 | 1 | 100% |
| 8 | Durand, D. E (1953) Country classification | 0.405 | 1 | 1 | 100% |
| 9 | Hafner-Burton, E. M., Kahler, M., and Montgomery, A. H (2009) Network analysis for international relations | 0.405 | 1 | 1 | 100% |
| 10 | Hanneke, S., Fu, W., and Xing, E. P (2010) Discrete temporal models of social networks | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.