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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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書(shū)目名稱(chēng)Machine-learning Techniques in Economics
副標(biāo)題New Tools for Predic
編輯Atin Basuchoudhary,James T. Bang,Tinni Sen
視頻videohttp://file.papertrans.cn/621/620805/620805.mp4
概述Offers a guide to how machine learning techniques can improve predictive power in answering economic questions.Provides R codes to help guide the researcher in applying machine learning techniques usi
叢書(shū)名稱(chēng)SpringerBriefs in Economics
圖書(shū)封面Titlebook: Machine-learning Techniques in Economics; New Tools for Predic Atin Basuchoudhary,James T. Bang,Tinni Sen Book 2017 The Author(s) 2017 Mach
描述This book develops a machine-learning framework for predicting economic growth. It can also be considered as a?primer for using machine learning (also 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.?.
出版日期Book 2017
關(guān)鍵詞Machine learning; Data mining; Economic growth; Prediction; Ranking predictive variables; Forecasting; Eco
版次1
doihttps://doi.org/10.1007/978-3-319-69014-8
isbn_softcover978-3-319-69013-1
isbn_ebook978-3-319-69014-8Series ISSN 2191-5504 Series E-ISSN 2191-5512
issn_series 2191-5504
copyrightThe Author(s) 2017
The information of publication is updating

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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.?.
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