SpringerBriefs in Economics
© 2017
Machine-learning Techniques in Economics
New Tools for Predicting Economic Growth
Authors: Basuchoudhary, Atin, Bang, James T., Sen, Tinni
Free Preview- 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 using the R package
- Uses partial dependence plots to tease out non-linear effects of explanatory variables on the dependent variables
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- About this book
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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.
- Table of contents (6 chapters)
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Why This Book?
Pages 1-6
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Data, Variables, and Their Sources
Pages 7-18
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Methodology
Pages 19-28
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Predicting a Country’s Growth: A First Look
Pages 29-36
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Predicting Economic Growth: Which Variables Matter
Pages 37-56
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Table of contents (6 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Machine-learning Techniques in Economics
- Book Subtitle
- New Tools for Predicting Economic Growth
- Authors
-
- Atin Basuchoudhary
- James T. Bang
- Tinni Sen
- Series Title
- SpringerBriefs in Economics
- Copyright
- 2017
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s)
- eBook ISBN
- 978-3-319-69014-8
- DOI
- 10.1007/978-3-319-69014-8
- Softcover ISBN
- 978-3-319-69013-1
- Series ISSN
- 2191-5504
- Edition Number
- 1
- Number of Pages
- VI, 94
- Number of Illustrations
- 1 b/w illustrations, 19 illustrations in colour
- Topics
