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Empirical Economics
Published in: Empirical Economics 4/2022

07-02-2022

Economic determinants of regional trade agreements revisited using machine learning

Authors: Simon Blöthner, Mario Larch

Published in: Empirical Economics | Issue 4/2022

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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.
previous article Effectiveness of monetary policy under the high and low economic uncertainty states: evidence from the major Asian economies
next article Exchange rate parities and Taylor rule deviations

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Appendix
Available only for authorised users
Footnotes
1
We provide a graph showing the development of the number of trade relationships with different types of RTAs over the years 1960–2019 in the Online Appendix.
 
2
Since Baier and Bergstrand (2004) many papers refined the specification, including for example neighboring effects like Egger and Larch (2008), Chen and Joshi (2010), Baldwin and Jaimovich (2012), and Baier et al. (2014) or political economy motives like Facchini et al. (2013), Maggi and Rodríguez-Clare (2007), Liu (2008), and Liu and Ornelas (2014). See Maggi (2014) for an excellent survey.
 
6
Note that \(\text{ DROWKL }\) is significant but positive for all RTAs in the cross section in Egger and Larch (2008). Hence, using more recent data and data for more countries seems to bring the probit estimates closer to the theory.
 
8
We provide formal demonstrations of these multi-collinearities in the Online Appendix.
 
9
A regression of \(\text{ REMOTE }\) on \(\text{ NATURAL }\) and importer and exporter fixed effects delivers and \(R^2\) from basically 1 and a residual standard error of 0.0008558.
 
12
\(\lceil \cdot \rceil \) denotes the ceiling function, which always rounds to the next largest integer.
 
14
Available for download at https://​cran.​r-project.​org/​web/​packages/​GA/​index.​html. Alternative ways to do automatic hyperparametrization optimization are Bayesian optimization or simple random search (see Chollet and Allaire 2018 for example).
 
15
Note that these are only possible explanations. It would be interesting in future research to dig deeper into the specific events and mechanisms.
 
18
While in the 1990s a big focus was on investigating the effect of different activation functions, the consensus nowadays is that a simple, computationally efficient nonlinear transformation is sufficient if one uses enough nodes and layers (see Taddy 2019).
 
19
The corresponding figure without fixed effects is in the Online Appendix as Figure A.8.
 
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Metadata
Title
Economic determinants of regional trade agreements revisited using machine learning
Authors
Simon Blöthner
Mario Larch
Publication date
07-02-2022
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 4/2022
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-022-02203-x

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