Economic Modeling Using Artificial Intelligence Methods

270 Pages·2013·3.14 MB·English

by  Tshilidzi Marwala (auth.)

Advanced Information and Knowledge Processing
Tshilidzi Marwala
Economic
Modeling
Using Artifi cial
Intelligence
Methods
Economic Modeling Using Artificial Intelligence
Methods
Advanced Information and Knowledge Processing
SeriesEditors
ProfessorLakhmiJain
[email protected]
ProfessorXindongWu
[email protected]
Forfurthervolumes:
http://www.springer.com/series/4738
Tshilidzi Marwala
Economic Modeling Using
Artificial Intelligence
Methods
123
TshilidziMarwala
FacultyofEngineeringandtheBuilt
Environment
UniversityofJohannesburg
Johannesburg
SouthAfrica
ISSN1610-3947
ISBN978-1-4471-5009-1 ISBN978-1-4471-5010-7(eBook)
DOI10.1007/978-1-4471-5010-7
SpringerLondonHeidelbergNewYorkDordrecht
LibraryofCongressControlNumber:2013935647
©Springer-VerlagLondon2013
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Foreword
Thequesttofindaneconomictheorythatisabletoexplainalleconomicactivities
hasalludedboththeoristsandpractitionersalike.Ifsuchatheorycanbediscussedit
willusheranewareawhereadesiredeconomicoutcomecanbeengineeredandthe
social dividendsthat can be harnessed from this are substantial. However,despite
thislimitationithasbeenquitepossibletomodelaspectsoftheeconomyseparately.
Forexample,itisquitepossibletomodelinflationandalsoengineerconceptssuch
asinflationtargeting.
Economic Modeling Using Artificial Intelligence Methods introduces the con-
ceptsofartificialintelligenceformodelingeconomicdata.Artificialinteligenceisa
scientifictechniquethatlooksathownatureoperatesandemulateit.Forexample,
how a human mind works or how a group of ants tackle problems. Artificial
intelligence is used particularly in areas where it is incredibly difficult to model
aphenomenon.
Therecenteconomiccrisishashighlightedtheneedtotreattheareaofeconomic
instrumentswith caution. For example complexderivativeshave been responsible
forthecollapseofbanksintheUSA.Thisbookdealswiththeareaofunderstanding
economicdata, modelingoptions, understandingeconomicgrowth,understanding
inflation, controlling inflation, optimizing a portfolio of investment assets and
modelling stock market. It also treats the area of interstate peace in promoting
economicactivities.
Thisbookgivesadifferentperspectiveofeconometrics.
Johannesburg,SouthAfrica AdamHabib,Ph.D.
March2013
v

Preface
EconomicModelingUsing ArtificialIntelligenceMethodsintroducesthe concepts
of artificial intelligence for modeling economic data. In this book, the artificial
intelligence techniques that are used to model economic data include neural
networks, support vector machines, rough sets, genetic algorithm, particle swarm
optimization, simulated annealing, multi-agent system, incremental learning and
fuzzy networks. In addition, this book explores signal processing techniques to
analyze economic data and to deal with vital subjects such as stationarity. These
signalprocessingtechniquesarethetimedomainmethods,time-frequencydomain
methodsandfractalsdimensionapproaches.
These techniques are used to solve interesting economic problems such as
causality versus correlation, modeling the stock market, modeling inflation and
portfoliooptimization.Inaddition,gametheoreticframeworkisusedtosimulatethe
stockmarketandcontrolsystemstechniqueisusedforinflationtargeting.Finally,
animportantareaoftherelationshipbetweeneconomicdependencyandinterstate
conflict is explored and some interesting insights on how economics can be used
to foster peace and vice versa are explored. This book specifically addresses the
issue of causality in the non-linear domain and applies the automatic relevance
determination,the evidenceframework,Bayesian approachand Grangercausality
toachievethisgoal.
Thisbookmakesanimportantcontributiontotheareaofeconometrics,andisan
interestingreadforgraduatestudents,researchersandfinancialpractitioners.
UniversityofJohannesburg TshilidziMarwala
Johannesburg,SouthAfrica
March2013
vii

Acknowledgements
I would like to thank the following former and current graduate students for
their assistance in developing this manuscript: Michael Pires, Msizi Khoza, Ish-
mael Sibusiso Msiza, Nadim Mohamed, Dr. Brain Leke, Dr. Sizwe Dhlamini,
ThandoTettey,BodieCrossingham,Prof.FulufheloNelwamondo,VukosiMarivate,
Dr.ShakirMohamed,Dr.BoXing,Dr.DavidStarfield,Dr.PreteshPatel,Dr.Dalton
Lunga,Dr. Linda Mthembu,Dr. Meir Perez, Dr. MeganRussell and Dr. Busisiwe
Vilakazi. I thank Mr Evan Hurwitz for developing the ideas in Chap. 9. I also
thankcolleaguesandpractitionersthathavecollaborateddirectlyandindirectlyto
the writing of the manuscript. In particular, I thank Dr. Ian Kennedy, Dr. Monica
Lagazio and the anonymous reviewers for their comments and careful reading of
the book. I thank my supervisors Dr. Hugh Hunt, Prof. Stephan Heyns and Prof.
PhilippedeWilde.
I dedicate this book to Dr. Jabulile Manana as well as my sons Lwazi Thendo
andNhloniphoKhathutshelo.
UniversityofJohannesburg TshilidziMarwala
Johannesburg,SouthAfrica
March2013
ix