Date Published:
Dec 26, 2018
Focus Area(s):
DP 2018-33

This paper seeks to understand certain frameworks that can be used to improve the analysis and prediction of trade flows within the Asia-Pacific Economic Cooperation economies using neural networks. Discussions include the history of neural network development, the biological neuron, the artificial neuron, and the potential use of neural networks in trade analysis. This paper also compares the different estimation procedures of the gravity model–-Ordinary Least Squares, Poisson Pseudo Maximum Likelihood, and Gamma Pseudo Maximum Likelihood-–with the neural network. Study finds that the neural network estimation of the gravity equation is superior over the other procedures in terms of explaining the variability of the dependent variable (export) around its mean and in terms of the accuracy of predictions.


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In other Publications
  1. Blöthner, Simon and Mario Larch. 2021. Economic determinants of Regional Trade Agreements revisited using machine learning. CESifo Working Paper Series 9233. CESifo.

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