Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth (SpringerBriefs in Economics)

Basuchoudhary, Atin; Bang, James T.; Sen, Tinni

ISBN 10: 3319690132 / ISBN 13: 9783319690131

Published by Springer, 2018

About this Item

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Bibliographic Details

• Title: Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth (SpringerBriefs in Economics)
• Publisher: Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3319690132
• ISBN 13: 9783319690131
• Binding: Soft cover
• Condition: good

About this title

Synopsis

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.