Machine Learning in International Trade Research: Evaluating the Impact of Trade Agreements
Machine Learning in International Trade Research: Evaluating the Impact of Trade Agreements
复制标题
国际贸易研究中的机器学习：评估贸易协定的影响
DOI：
10.1596/1813-9450-9629
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Thomas Zylkin
中科院分区：
文献类型：
--
作者：
Holger Breinlich;V. Corradi;N. Rocha;M. Ruta;J.M.C. Santos Silva;Thomas Zylkin
现代贸易协定除削减关税外，还在服务贸易、竞争政策、与贸易有关的投资措施或公共采购等不同领域载有大量条款。现有的研究在试图估计这些条款对贸易流量的影响时，一直在努力解决过度拟合和严重的多重共线性问题。在本文中，我们开发了一种新的方法来估计个别条款对贸易流量的影响，不需要特别假设如何合计个别条款。基于机器学习和变量选择文献的最新发展，我们提出了数据驱动的方法来选择最重要的条款并量化其对贸易流的影响。我们发现，有关反倾销，竞争政策，技术性贸易壁垒和贸易便利化的规定与贸易协定的贸易增长效果的增强。
Modern trade agreements contain a large number of provisions besides tariff reductions, in areas as diverse as services trade, competition policy, trade-related investment measures, or public procurement. Existing research has struggled with overfitting and severe multicollinearity problems when trying to estimate the effects of these provisions on trade flows. In this paper, we develop a new method to estimate the impact of individual provisions on trade flows that does not require ad hoc assumptions on how to aggregate individual provisions. Building on recent developments in the machine learning and variable selection literature, we propose data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. We find that provisions related to antidumping, competition policy, technical barriers to trade, and trade facilitation are associated with enhancing the trade-increasing effect of trade agreements.
三向重力模型的偏差和一致性
影响因子：
3.3
作者：
Weidner M
通讯作者：
Weidner M
影响因子：
5.8
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Rob
通讯作者：
Tibshirani, Rob