Please use this identifier to cite or link to this item: https://doi.org/10.3390/su11051449
Title: Estimating China's trade with its partner countries within the belt and road initiative using neural network analysis
Authors: Dumor, K.
Yao, L. 
Keywords: Belt and road initiative
Gravity model
Neural network analysis
Trade
Issue Date: 2019
Publisher: MDPI AG
Citation: Dumor, K., Yao, L. (2019). Estimating China's trade with its partner countries within the belt and road initiative using neural network analysis. Sustainability (Switzerland) 11 (5) : 1449. ScholarBank@NUS Repository. https://doi.org/10.3390/su11051449
Rights: Attribution 4.0 International
Abstract: The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared. © 2019 by the authors.
Source Title: Sustainability (Switzerland)
URI: https://scholarbank.nus.edu.sg/handle/10635/209993
ISSN: 2071-1050
DOI: 10.3390/su11051449
Rights: Attribution 4.0 International
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