
Machine-learning Techniques in Economics
New Tools for Predicting Economic Growth
Springer (Publisher)
Published on 8. January 2018
Book
Paperback/Softback
VI, 94 pages
978-3-319-69013-1 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
1 s/w Abbildung, 19 farbige Abbildungen
VI, 94 p. 20 illus., 19 illus. in color.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
226 gr
ISBN-13
978-3-319-69013-1 (9783319690131)
DOI
10.1007/978-3-319-69014-8
Schweitzer Classification
Other editions
Additional editions

Atin Basuchoudhary | James T. Bang | Tinni Sen
Machine-learning Techniques in Economics
New Tools for Predicting Economic Growth
E-Book
12/2017
Springer
€69.54
Available for download
Content
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.