Titlebook: Machine-learning Techniques in Economics; New Tools for Predic Atin Basuchoudhary,James T. Bang,Tinni Sen Book 2017 The Author(s) 2017 Mach

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书目名称Machine-learning Techniques in Economics影响因子(影响力)学科排名




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书目名称Machine-learning Techniques in Economics网络公开度学科排名




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书目名称Machine-learning Techniques in Economics被引频次学科排名




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书目名称Machine-learning Techniques in Economics年度引用学科排名




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书目名称Machine-learning Techniques in Economics读者反馈学科排名

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Predicting Recessions: What We Learn from Widening the Goalposts,ct” growth variables to check whether these variables are better at predicting recessions. We show how prediction performance of algorithms differs widely depending on the type of prediction criteria. We can, however, identify some of the most salient predictors of recessions. These suggest that fis

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,Predicting a Country’s Growth: A First Look,o validate different growth models. We suggest that validated algorithms enhance the confidence academics should place on any given theoretical growth model. We then show how machine learning can help researchers understand what kinds of concepts may make theoretical growth models more complete.

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Book 2017 known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists. .