International Trade Network: Statistical Analysis and Modeling
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910611139461120 |
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| author | Sosa, Juan Arévalo-Arévalo, Andrés Felipe Torres-Clavijo, Juan Pablo |
| author_facet | Sosa, Juan Arévalo-Arévalo, Andrés Felipe Torres-Clavijo, Juan Pablo |
| contents | Globalization has rapidly advanced but exposed countries to supply chain disruptions, highlighted by the COVID-19 pandemic. This study exhaustively analyzes bilateral export data for 186 countries from 2018, 2020, and 2022, using Exponential Random Graph Models (ERGMs), to identify determinants of trade relationships, as well as Stochastic Block Models (SBMs), to characterize countries' roles in the trade network. Our findings show persistent, significant nodal characteristics driving bilateral trade and reveal no major structural changes in the trade network due to the pandemic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12358 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | International Trade Network: Statistical Analysis and Modeling Sosa, Juan Arévalo-Arévalo, Andrés Felipe Torres-Clavijo, Juan Pablo Applications Globalization has rapidly advanced but exposed countries to supply chain disruptions, highlighted by the COVID-19 pandemic. This study exhaustively analyzes bilateral export data for 186 countries from 2018, 2020, and 2022, using Exponential Random Graph Models (ERGMs), to identify determinants of trade relationships, as well as Stochastic Block Models (SBMs), to characterize countries' roles in the trade network. Our findings show persistent, significant nodal characteristics driving bilateral trade and reveal no major structural changes in the trade network due to the pandemic. |
| title | International Trade Network: Statistical Analysis and Modeling |
| topic | Applications |
| url | https://arxiv.org/abs/2409.12358 |