Modeling And Advanced Techniques In Modern Economics

328 Pages·2022·15.787 MB·English

by  Çağdaş Hakan Aladağ, Nihan Potas

Modeling Advanced
and
Techniques Modern
in
Economics
B1948 Governing Asia
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Modeling Advanced
and

Techniques Modern
in

Economics
Editors
Çağdaş Hakan Aladağ
Hacettepe University, Turkey
Nihan Potas
Ankara Hacı Bayram Veli University, Turkey
World Scientific
NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO
Published by
World Scientific Publishing Europe Ltd.
57 Shelton Street, Covent Garden, London WC2H 9HE
Head office: 5 Toh Tuck Link, Singapore 596224
USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601
Library of Congress Cataloging-in-Publication Data
Names: Aladağ, Çağdaş Hakan, editor. | Potas, Nihan, editor.
Title: Modeling and advanced techniques in modern economics / editors,
Çağdaş Hakan Aladağ, Hacettepe University, Turkey,
Nihan Potas, Ankara Hacı Bayram Veli University, Turkey.
Description: New Jersey : World Scientific, [2022] |
Includes bibliographical references and index.
Identifiers: LCCN 2022000600 | ISBN 9781800611740 (hardcover) |
ISBN 9781800611757 (ebook) | ISBN 9781800611764 (ebook other)
Subjects: LCSH: Econometric models. | Economics--Mathematical models.
Classification: LCC HB141 .M586 2022 | DDC 330.01/5195--dc23/eng/20220118
LC record available at https://lccn.loc.gov/2022000600
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Copyright © 2022 by World Scientific Publishing Europe Ltd.
All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means,
electronic or mechanical, including photocopying, recording or any information storage and retrieval
system now known or to be invented, without written permission from the Publisher.
For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance
Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy
is not required from the publisher.
For any available supplementary material, please visit
https://www.worldscientific.com/worldscibooks/10.1142/Q0346#t=suppl
Desk Editor: Soundararajan Raghuraman
Typeset by Stallion Press
Email: [email protected]
Printed in Singapore
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To my mother Rezzan Alada˘g
and
my father Hikmet Feridun Alada˘g
C¸a˘gda¸s Hakan Alada˘g
To my mother Prof. Dr. S¸efika S¸ule Er¸cetin,
my sister Dr. S¸uay Nilhan A¸cıkalın
and
my beloved children Mihri and Uraz for their eternal love
Nihan Potas
v
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(cid:2)c 2022 World Scientific Publishing Europe Ltd.
https://doi.org/10.1142/9781800611757 fmatter
Preface
While the global economy stumbles under a pandemic crisis, under-
standing macroeconomic variables has become more vital not only
for the governments and economy bureaucracies but also for firms
and individuals. Thus, each of us needs to understand the impact of
exogenous shocks on the global economy. Statistical thinking is one
of the leading ways of improvement in this thinking. For statistical
thinking, data that can be worked on and then, in order to make
clear, understandable and explanatory results, statistical inference
are required.
The way to shape and see the future is to collect data correctly,
analyze it correctly and interpret the results correctly. There are
two main elements at the base of this approach. These are Statistics
and Economics. The aim of this book, briefly, is to introduce new
approaches that can be used to shape and forecast the future by
combining the two disciplines mentioned.
In this day and age, data is a vital asset for any organization,
regardless of industry or size. The world is built upon data. It is not
only data but also having the right kind of knowledge is critically
important today. In order to obtain the right kind of knowledge
from data, the process of analyzing data is very crucial for indi-
viduals, organizations or governments. This is why the concept of
Data Science is so popular today. Nowadays, many researchers and
practitioners from various fields are studying advanced data analy-
sis techniques. One of these fields is Economics. For example, time
series analysis is very important for economics. Time series, which
vii
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viii Modeling and Advanced Techniques in Modern Economics
is a collection of data points collected at constant time intervals, is
one of the most important data types. People have tried to forecast
time series by different methods for a long time. Forecasting is so
essential since the prediction of future events is a critical input for
many types of planning decision-making processes, with real-world
applications in all areas. With the development of technology and
algorithms, time series forecasting approaches are developing. Thus,
it is possible to reach more accurate predictions of the future by
using advanced forecasting approaches. In this manner, some chap-
tersofthisbookareintendedtobeavaluablesourceofrecentknowl-
edge on advanced time series forecasting techniques. These chapters
include applications of efficient, recent forecasting approaches. The
readers can also findusefulinformation on advanced time series fore-
casting techniques, such as artificial neural networks, deep learning,
machine learning and chaotic time series. In addition to these time
series applications, some chapters introduce some other recent data
analysis methods such as fiducialmethod,a novel approach based on
inverseGaussiandistributionandweightedsuperpositionattraction–
repulsion algorithm.
Trends in econometric analysis, especially in financial time series
analysis, are moving toward artificial intelligence. Machine learn-
ing becomes handier when dealing with complex structures as their
computational capacities increase. Moreover, in classical time series
analysis, univariate or multivariate, linear time series methods may
not capture these complex structures. Nowadays, deep neural net-
works are a popular tool in forecasting and classification. Neural
networks are flexible and can be used in both regression and clas-
sification. They have good performance in predicting nonlinear and
non-stationary time series. The learning algorithms used in financial
time series or econometrics are not restricted to neural networks.
In classification support vector machines, tree type algorithms and
k-nearest neighbors are some of the most popular algorithms. In the
regression part support vector regression, ridge regression, Elastic-
Net regression and Lasso regression can be mentioned as some of the
popular algorithms. In light of these, in some chapters of this book,
the authors provide information on econometric and financial mod-
els as well as commonly used financial and economic variables and
mention sources of relevant data. The readers can also find useful
information about the financial and econometric models, stochastic
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Preface ix
financial models, machine learning and application of the models to
financial and macroeconomic data.
Readers can also find useful information about the effect of the
economy through theoretical, comparative and applied studies on
agent-based models, nonlinear economic systems, evolutionary eco-
nomics,econophysics,chaostheory,fractalanalysis,neuroeconomics,
fuzzy systems and network theory.
Economic agents, be they banks, firms, households, consumers or
investors, act with strategic behavior and foresight by considering
outcomes that might result from behavior they might undertake.
Furthermore,theycontinuallyadjusttheirmarketoperations,buying
decisions,pricesandforecaststothesituationsthattheseoperations,
decisions, prices and forecasts together create. These topics add a
layer of complexity to economics not found in the natural sciences.
The editors would also like to express their sincere thanks to all
authors for their valuable contributions. We believe that this book is
a very useful resource for the readers.