Machine-learning techniques in economics : new tools for predicting economic growth / Atin Basuchoudary, James T. Bang, Tinni Sen.
2017
HD82 .B37 2017
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Details
Title
Machine-learning techniques in economics : new tools for predicting economic growth / Atin Basuchoudary, James T. Bang, Tinni Sen.
Author
Basuchoudhary, Atin, author.
ISBN
9783319690148 (electronic book)
3319690140 (electronic book)
9783319690131
3319690132
3319690140 (electronic book)
9783319690131
3319690132
Published
Cham, Switzerland : Springer, [2017]
Language
English
Description
1 online resource : illustrations.
Item Number
10.1007/978-3-319-69014-8 doi
Call Number
HD82 .B37 2017
Dewey Decimal Classification
338.9
Summary
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. .-- Provided by publisher.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Vendor-supplied metadata.
Added Author
Bang, James T., author.
Sen, Tinni, author.
Sen, Tinni, author.
Series
SpringerBriefs in economics.
Available in Other Form
Machine-learning techniques in economics.
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Online Access
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Table of Contents
Why this Book?
Data, Variables, and Their Sources
Methodology
Predicting Economic Growth: A First Look
Predicting Economic Growth: Which Variables Matter?
Predicting Recessions: What We Learn from Widening the Goalposts
Epilogue.
Data, Variables, and Their Sources
Methodology
Predicting Economic Growth: A First Look
Predicting Economic Growth: Which Variables Matter?
Predicting Recessions: What We Learn from Widening the Goalposts
Epilogue.