Machine-learning Techniques in Economics New Tools for Predicting Ec
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
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Atin Basuchoudhary James T. Bang Tinni Sen
Machine-learning Techniques in Economics New Tools for Predicting Economic Growth 123
SpringerBriefs in Economics
More information about this series at http://www.springer.com/series/8876
Atin Basuchoudhary • James T. Bang • Tinni Sen
Machine-learning Techniques in Economics New Tools for Predicting Economic Growth
Atin Basuchoudhary Department of Economics and Business Virginia Military Institute Lexington, VA, USA
James T. Bang Department of Finance, Economics, and Decision Science St. Ambrose University Davenport, IA, USA
Tinni Sen Department of Economics and Business Virginia Military Institute Lexington, VA, USA
ISSN 2191-5504 ISSN 2191-5512 (electronic) SpringerBriefs in Economics ISBN 978-3-319-69013-1 ISBN 978-3-319-69014-8 (eBook) https://doi.org/10.1007/978-3-319-69014-8 Library of Congress Control Number: 2017955621 © The Author(s) 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
1
Why This Book? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 6
2
Data, Variables, and Their Sources . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Variables and Their Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Problems with Institutional Measures . . . . . . . . . . . . . . . . . . . . 2.3 Imputing Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . .
7 12 15 18 18
3
Methodology . . . .
Data Loading...
