Ovielt Baltodano López, Roberto Casarin
arXiv 19 Sep 2022 · Statistics — Methodology
arXiv:2209.09354 · PDF · DOI · OpenAlex · Extracted main text
The availability of relational data can offer new insights into the functioning of the economy. Nevertheless, modeling the dynamics in network data with multiple types of relationships is still a challenging issue. Stochastic block models provide a parsimonious and flexible approach to network analysis. We propose a new stochastic block model for multidimensional networks, where layer-specific hidden Markov-chain processes drive the changes in community formation. The changes in the block membership of a node in a given layer may be influenced by its own past membership in other layers. This allows for clustering overlap, clustering decoupling, or more complex relationships between layers, including settings of unidirectional, or bidirectional, non-linear Granger block causality. We address the overparameterization issue of a saturated specification by assuming a Multi-Laplacian prior distribution within a Bayesian framework. Data augmentation and Gibbs sampling are used to make the inference problem more tractable. Through simulations, we show that standard linear models and the pairwise approach are unable to detect block causality in most scenarios. In contrast, our model can recover the true Granger causality structure. As an application to international trade, we show that our model offers a unified framework, encompassing community detection and Gravity equation modeling. We found new evidence of block Granger causality of trade agreements and flows and core-periphery structure in both layers on a large sample of countries.
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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 | Matias, C. and Miele, V (2017) Statistical clustering of temporal networks through a dynamic stochastic block model | 1.000 | 5 | 3 | 100% |
| 2 | Yang, T., Chi, Y., Zhu, S., Gong, Y., and Jin, R (2011) Detecting communities and their evolutions in dynamic social networks—a Bayesian approach | 0.928 | 4 | 3 | 100% |
| 3 | Raman, S., Fuchs, T. J., Wild, P. J., Dahl, E., and Roth, V (2009) The Bayesian group-lasso for analyzing contingency tables | 0.843 | 5 | 3 | 60% |
| 4 | Helpman, E., Melitz, M., and Rubinstein, Y (2008) Estimating trade flows: Trading partners and trading volumes | 0.811 | 4 | 2 | 100% |
| 5 | Barigozzi, M., Fagiolo, G., and Mangioni, G (2011) Identifying the community structure of the international-trade multi-network | 0.737 | 3 | 2 | 100% |
| 6 | Frühwirth-Schnatter, S (2006) Finite mixture and Markov switching models, volume 425 | 0.737 | 3 | 2 | 100% |
| 7 | Silva, J. S. and Tenreyro, S (2006) The log of Gravity | 0.644 | 4 | 1 | 100% |
| 8 | Baier, S. L. and Bergstrand, J. H (2007) Do free trade agreements actually increase members' international trade? | 0.644 | 2 | 2 | 100% |
| 9 | Baier, S. L., Yotov, Y. V., and Zylkin, T (2019) On the widely differing effects of free trade agreements: Lessons from twenty years of trade integration | 0.644 | 2 | 2 | 100% |
| 10 | Bartesaghi, P., Clemente, G. P., and Grassi, R (2020) Communicability in the world trade network–a new perspective for community detection | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 82 scored citations.