Regional economic integration and machine learning: Policy insights from the review of literature

被引:3
作者
De Lombaerde, Philippe [1 ,2 ,3 ]
Naeher, Dominik [4 ]
Vo, Hung Trung [5 ]
Saber, Takfarinas [6 ]
机构
[1] United Nat Univ, Inst Comparat Reg Integrat Studies UNU CRIS, Brugge, Belgium
[2] Neoma Business Sch, Rouen, France
[3] Vrije Univ Brussel, Brussels, Belgium
[4] Univ Gottingen, Dept Dev Econ, Gottingen, Germany
[5] Thu Dau Mot Univ, Fac Econ, Thu Dau Mot, Vietnam
[6] Univ Galway, Lero Irish Software Res Ctr, Sch Comp Sci, Galway, Ireland
基金
爱尔兰科学基金会;
关键词
Regional economic integration; International trade; Machine learning; Artificial intelligence; Literature review; COMPLEX NETWORK; MONETARY-UNION; TRADE; COUNTRIES; PATTERNS;
D O I
10.1016/j.jpolmod.2023.07.001
中图分类号
F [经济];
学科分类号
02 ;
摘要
Due to its focus on prediction rather than causal inference, machine learning has long been treated somewhat neglectfully in the economic literature. For several reasons, however, interest in machine learning has surged recently and is slowly finding its way into the econometric toolbox. Within the economic literature, regional integration has been one of the research areas at the forefront of this development, with various studies experimenting with different machine learning techniques to shed light on the complex dynamics governing regional integration processes. This paper provides the first systematic review of the literature that uses machine learning to study regional economic integration. The focus is twofold, first analysing studies along various thematic and methodological features (and the links between them), and then discussing the scope and nature of policy insights derived from the surveyed body of literature. (c) 2023 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. JEL classification: C30; C60; F02; F15; F60
引用
收藏
页码:1077 / 1097
页数:21
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