区域经济一体化与机器学习:来自文献回顾的政策见解

IF 3.5 2区 经济学 Q1 ECONOMICS
Philippe De Lombaerde , Dominik Naeher , Hung Trung Vo , Takfarinas Saber
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引用次数: 0

摘要

由于机器学习的重点是预测而不是因果推理,长期以来,机器学习在经济文献中一直被忽视。然而,由于几个原因,最近人们对机器学习的兴趣激增,并且正在慢慢地进入计量经济学工具箱。在经济文献中,区域一体化一直是这一发展的前沿研究领域之一,各种研究尝试了不同的机器学习技术,以阐明控制区域一体化过程的复杂动态。本文首次对利用机器学习研究区域经济一体化的文献进行了系统回顾。重点是双重的,首先分析各种主题和方法特征的研究(以及它们之间的联系),然后讨论从被调查的文献中得出的政策见解的范围和性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
6.20
自引率
11.40%
发文量
76
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.
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