Philippe De Lombaerde , Dominik Naeher , Hung Trung Vo , Takfarinas Saber
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Regional economic integration and machine learning: Policy insights from the review of literature
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.
期刊介绍:
The Journal of Policy Modeling is published by Elsevier for the Society for Policy Modeling to provide a forum for analysis and debate concerning international policy issues. The journal addresses questions of critical import to the world community as a whole, and it focuses upon the economic, social, and political interdependencies between national and regional systems. This implies concern with international policies for the promotion of a better life for all human beings and, therefore, concentrates on improved methodological underpinnings for dealing with these problems.