Economic determinants of regional trade agreements revisited using machine learning

Simon Blöthner () and Mario Larch
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Simon Blöthner: University of Bayreuth

Empirical Economics, 2022, vol. 63, issue 4, No 2, 1807 pages

Abstract: Abstract While traditional empirical models using determinants like size and trade costs can predict RTA formation reasonably well, we demonstrate that allowing for machine-detected nonlinear patterns helps to improve the predictive power of RTA formation substantially. We find that the fitted tree-based methods and neural networks deliver sharper and more accurate predictions than the probit model. For the majority of models, the allowance of fixed effects increases the predictive performance considerably. We apply our models to predict the likelihood of RTA formation of the EU and the USA with their trading partners, respectively.

Keywords: Regional trade agreements; Neural networks; Tree-based methods; High-dimensional fixed effects (search for similar items in EconPapers)
JEL-codes: C45 C53 F14 F15 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00181-022-02203-x

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