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Machine-learning Techniques in Economics

New Tools for Predicting Economic Growth

  • Atin Basuchoudhary
  • James T. Bang
  • Tinni Sen

Part of the SpringerBriefs in Economics book series (BRIEFSECONOMICS)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 1-6
  3. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 7-18
  4. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 19-28
  5. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 29-36
  6. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 37-56
  7. Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 57-73
  8. Back Matter
    Pages 75-91

About this book

Introduction

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. 

Keywords

Machine learning Data mining Economic growth Prediction Ranking predictive variables Forecasting Econometrics

Authors and affiliations

  • Atin Basuchoudhary
    • 1
  • James T. Bang
    • 2
  • Tinni Sen
    • 3
  1. 1.Department of Economics and BusinessVirginia Military InstituteLexingtonUSA
  2. 2.Department of Finance, Economics, and Decision ScienceSt. Ambrose UniversityDavenportUSA
  3. 3.Department of Economics and BusinessVirginia Military InstituteLexingtonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-69014-8
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Economics and Finance
  • Print ISBN 978-3-319-69013-1
  • Online ISBN 978-3-319-69014-8
  • Series Print ISSN 2191-5504
  • Series Online ISSN 2191-5512
  • Buy this book on publisher's site