IMF-高性能出口产品组合：利用机器学习设计增长促进出口结构 High Performance Export Portfolio： Design Growth- Enhancing Export Structure with Machine Learning 2022.pptx

High Performance Export Portfolio: Design Growth- Enhancing Export Structure with Machine Learning Natasha Che, Xuege Zhang WP/22/75 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2022 APR ABSTRACT: This paper studies the relationship between export structure and growth performance. We design an export recommendation system using a collaborative filtering algorithm based on countries revealed comparative advantages. The system is used to produce export portfolio recommendations covering over 190 economies and over 30 years. We find that economies with their export structure more aligned with the recommended export structure achieve better growth performance, in terms of both higher GDP growth rate and lower growth volatility. These findings demonstrate that export structure matters for obtaining high and stable growth. Our recommendation system can serve as a practical tool for policymakers seeking actionable insights on their countries’ export potential and diversification strategies that may be complex and hard to quantify. IMF Working Paper Asia Pacific Department High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning Prepared by Natasha Che, Xuege Zhang* Authorized for distribution by Shanaka Jayanath Peiris April 2022 JEL Classification Numbers: F1, F4, O1, O4 Keywords: export diversification, comparative advantage, machine learning, collaborative filtering, economic growth, international trade IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Pa