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

Philippe De Lombaerde, Dominik Naeher, Hung Trung Vo and Takfarinas Saber

Journal of Policy Modeling, 2023, vol. 45, issue 5, 1077-1097

Abstract: 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.

Keywords: Regional economic integration; International trade; Machine learning; Artificial intelligence; Literature review (search for similar items in EconPapers)
JEL-codes: C30 C60 F02 F15 F60 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jpolmo:v:45:y:2023:i:5:p:1077-1097

DOI: 10.1016/j.jpolmod.2023.07.001

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