全球智库信息集成服务系统(G2TT): DP17325 Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP17325
	DP17325 Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements
	Holger Breinlich; Valentina Corradi; Nadia Rocha; Michele Ruta; Thomas Zylkin; JMC Santos Silva
发表日期	2022-05-23
出版年	2022
语种	英语
摘要	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 build on recent developments in the machine learning and variable selection literature to propose novel data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. The proposed methods have the advantage of not requiring ad hoc assumptions on how to aggregate individual provisions and offer improved selection accuracy over the standard lasso. We find that provisions related to technical barriers to trade, antidumping, trade facilitation, subsidies, and competition policy are associated with enhancing the trade-increasing effect of trade agreements.
主题	International Trade and Regional Economics
关键词	Lasso Machine learning Preferential trade agreements Deep trade agreements
URL	https://cepr.org/publications/dp17325
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/546358
推荐引用方式 GB/T 7714	Holger Breinlich,Valentina Corradi,Nadia Rocha,et al. DP17325 Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements. 2022.