Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth
by Atin Basuchoudhary (Author),‎ James T. Bang (Author),‎ Tinni Sen (Author)

About this book
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.

Table of contents

• Front Matter
• Why This Book?
• Data, Variables, and Their Sources
• Methodology
• Predicting a Country’s Growth: A First Look
• Predicting Economic Growth: Which Variables Matter
• Predicting Recessions: What We Learn from Widening the Goalposts
• Back Matter
  

Series: SpringerBriefs in Economics
Paperback: 94 pages
Publisher: Springer; 1st ed. 2017 edition (December 28, 2017)
Language: English

About the series
SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical topics might include:

• A timely report of state-of-the art analytical techniques
• A bridge between new research results, as published in journal articles, and a contextual literature review
• A snapshot of a hot or emerging topic
• An in-depth case study or clinical example
• A presentation of core concepts that students must understand in order to make independent contributions
  

SpringerBriefs in Economics showcase emerging theory, empirical research, and practical application in microeconomics, macroeconomics, economic policy, public finance, econometrics, regional science, and related fields, from a global author community.