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Machine-learning Techniques in Economics

New Tools for Predicting Economic Growth

  • Book
  • © 2017

Overview

Authors:
  1. Atin Basuchoudhary

    1. Department of Economics and Business, Virginia Military Institute, Lexington, USA

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  2. James T. Bang

    1. Department of Finance, Economics, and Decision Science, St. Ambrose University, Davenport, USA

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    You can also search for this author in PubMed   Google Scholar

  3. Tinni Sen

    1. Department of Economics and Business, Virginia Military Institute, Lexington, USA

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    You can also search for this author in PubMed   Google Scholar

  • Offers a guide to how machine learning techniques can improve predictive power in answering economic questions
  • Provides R codes to help guide the researcher in applying machine learning techniques using the R package
  • Uses partial dependence plots to tease out non-linear effects of explanatory variables on the dependent variables

Part of the book series: SpringerBriefs in Economics (BRIEFSECONOMICS)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-vi
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  2. Why This Book?

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 1-6

  3. Data, Variables, and Their Sources

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 7-18

  4. Methodology

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 19-28

  5. Predicting a Country’s Growth: A First Look

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 29-36

  6. Predicting Economic Growth: Which Variables Matter

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 37-56

  7. Predicting Recessions: What We Learn from Widening the Goalposts

    • Atin Basuchoudhary, James T. Bang, Tinni Sen
    Pages 57-73

  8. Back Matter

    Pages 75-91
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Keywords

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. 

Authors and Affiliations

  • Department of Economics and Business, Virginia Military Institute, Lexington, USA

    Atin Basuchoudhary, Tinni Sen

  • Department of Finance, Economics, and Decision Science, St. Ambrose University, Davenport, USA

    James T. Bang

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