Authors:
- 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
Part of the book series: SpringerBriefs in Economics (BRIEFSECONOMICS)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Department of Economics and Business, Virginia Military Institute, Lexington, USA
Atin Basuchoudhary, Tinni Sen
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Department of Finance, Economics, and Decision Science, St. Ambrose University, Davenport, USA
James T. Bang
Bibliographic Information
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
DOI: https://doi.org/10.1007/978-3-319-69014-8
Publisher: Springer Cham
eBook Packages: Economics and Finance, Economics and Finance (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-69013-1Published: 08 January 2018
eBook ISBN: 978-3-319-69014-8Published: 28 December 2017
Series ISSN: 2191-5504
Series E-ISSN: 2191-5512
Edition Number: 1
Number of Pages: VI, 94
Number of Illustrations: 1 b/w illustrations, 19 illustrations in colour
Topics: Economic Growth, Econometrics, Data Mining and Knowledge Discovery, Game Theory, Computer Appl. in Social and Behavioral Sciences