Creating a Repository of Economic Models

For Research and Education

An Interactive Qualifying Project (IQP) Submitted to the Faculty of

WORCESTER POLYTECHNIC INSTITUTE

In Partial Fulfillment of the Requirement for the

Degree of Bachelor of Science

Submitted by: Nicholas Fajardo Mitchell Farren

Synella Gonzales Mark Karpukhin

Artemii Maksimenko Yaofeng Wang

11 October, 2017

Submitted to:

Project Advisor

Svetlana Nikitina

Project Sponsor The Financial University under the Government of the Russian Federation

On-Site Liaison

Professor Anton Losev Department of Data Analysis, Decision-Making Theory and Financial Technology

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Abstract TheFinancialUniversityundertheGovernmentoftheRussianFederationhas

expressedinterestindevelopinganeducationaltooltohelpstudentsandresearchersaccesseconomicmodels.Toreachthisgoal,weconductedextensiveresearchonavarietyofmodels,administeredasurveytoseepotentialinterestinaneconomicrepositoryanditscomponents,codedresultsintoarepository,anddevelopedaWikitofostereducationalapplicationsofarepository.Asaresult,wecompiledaversionofarepositorywhichcouldserveasaframeworkforfuturedevelopmentofeducationalandresearchapplications.

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Acknowledgements Wewouldliketothankthefollowingpeoplefortheirhelpandguidancethroughoutthisproject: ProfessorAntonLosevforalltheguidance,feedback,andhelphehasprovidedus.Hehasgivenusanexperiencethatweallwillremember.Betweenworkingwithusonourproject,providingresources,showinguslocalculture,andremainingonstandbytoansweranyquestionswemighthavehad,ProfessorAntonLosevgaveusmorethanwecouldhaveeverhoped.Withouthim,nothingwedidwouldhavebeenpossible. ProfessorSvetlanaNikitinaforbeingthebestpossibleprojectadvisorwecouldhavehopedfor.Shesharedhertime,dedication,valuableinput,andlanguageandwritingskillstous,whichhelpedourprojectreachitsmaximumpotential. SvetlanaChugunovaforherhospitalityattheFinancialUniversityandshowingusRussianculture,makingourstayinRussiaonetoremember. ZalyaNartokovaforheradministrativehelpgettingtheprojectofftheground,andforshowingusaroundthecityofMoscow. ProfessorOlegPavlov,ProfessorElkeRundensteiner,andProfessorKhalidSaeedfortakingtimetoletusinterviewthemandgiveusafoundationalunderstandingofourprojectbeforewearrivedinRussia.Becauseofthem,wewereabletohitthegroundrunningoncewearrived.

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Authorship

Section PrimaryAuthor Editor(s)

ExecutiveSummary NicholasFajardo All

Introduction YaofengWang SynellaGonzales

Background SynellaGonzales NicholasFajardo,MitchellFarren,YaofengWang

Methodology NicholasFajardo MitchellFarren,SynellaGonzales,YaofengWang,

ResultsandDiscussion

SynellaGonzales

NicholasFajardo,MitchellFarren,YaofengWang

ConclusionandRecommendations

MitchellFarren NicholasFajardo,SynellaGonzales,YaofengWang

References YaofengWang SynellaGonzales

AppendixA-SponsorDescription

NicholasFajardo MitchellFarren,SynellaGonzales,YaofengWang,

AppendixB-InterviewProtocol

NicholasFajardo,MitchellFarren,SynellaGonzales,YaofengWang

All

AppendixC-DashboardQuestionnaire

NicholasFajardo,MitchellFarren,SynellaGonzales,MarkKarpukhin,ArtemiiMaksimenko,YaofengWang

All

AppendixD-ModelDescriptions

NicholasFajardo,MitchellFarren,SynellaGonzales,MarkKarpukhin,ArtemiiMaksimenko,YaofengWang

All

iv

Table of Contents Abstract.............................................................................................................................................iAcknowledgements......................................................................................................................iiAuthorship......................................................................................................................................iiiTable of Figures.............................................................................................................................vTable of Tables..............................................................................................................................viExecutive Summary....................................................................................................................vii1. Frontiers in Russian Economics Studies..........................................................................12. Economic Models: Scope, Significance, and Applications.........................................3The Russian economy’s unique path and need for better modeling .................................... 3 How does education and research support economic growth? ............................................ 5 What are the current methods of economic study? ................................................................ 5 What are economic models? ..................................................................................................... 5 Why are models important? ...................................................................................................... 6 How will these models be accessed by university staff and students? ............................... 6 What are current methods used to study and analyze economic models? ......................... 7 3. Methodology: Program Fabrication....................................................................................8Economic Model Research ........................................................................................................ 8 Programming the models .......................................................................................................... 9 Organizing and storing the models ........................................................................................ 10 Front-end framework ................................................................................................................ 10 Survey Development ................................................................................................................ 11 Third Party Tools and Packages ............................................................................................. 11 4. Results and Discussion........................................................................................................13Survey Results ......................................................................................................................... 13 Creating the framework ........................................................................................................... 14 Study Limitations ..................................................................................................................... 15 5. Conclusion and Recommendations..................................................................................17Recommendations ................................................................................................................... 17 References....................................................................................................................................18Appendices...................................................................................................................................21Appendix A: Sponsor Information .......................................................................................... 21 Appendix B: Interview Protocol .............................................................................................. 23 Appendix C: Dashboard Questionnaire ................................................................................. 25 Appendix D: Model Descriptions ............................................................................................ 26

World Trade Model............................................................................................................................26Cobb-Douglas Production Function Model................................................................................29Portfolio Model..................................................................................................................................30Scoring Model....................................................................................................................................32

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Table of Figures Figure1:TimespentbyEasternEuropeancountriesinatransitionalrecessionafterthefall

oftheSovietUnion(inyears). .............................................................................................. 3Figure2:Russia'scurrentGrossDomesticProduct(GDP) ........................................................ 4Figure3:Russia'scurrentGDPgrowth. ....................................................................................... 4Figure4:Mechanicsofarepository ............................................................................................. 7Figure5:Surveyquestions ......................................................................................................... 11Figure 6: Survey results – gauging the audience .......................................................................... 13Figure7:Surveyresults–decidingwhattoincludeintherepository ................................... 14Figure8:CurrentimplementationoftheWiki ......................................................................... 15Figure9:FinancialUniversityDepartmentStructure .............................................................. 21Figure10:CLIInterfaceforWorldTradeModel ...................................................................... 26Figure11:Calledfunctionscorrespondingtopossiblequeries .............................................. 27Figure12:Queryfortop5countriesin2003,accordingtoTradeIndex ............................... 27Figure13:Regressionresultsona3Dsurface .......................................................................... 29Figure14:Formulasforoptimizationcalculations .................................................................. 30Figure15:Efficientfrontierofrandomlygeneratedportfolios .............................................. 31Figure16:FormatforCvxOptPythonfunction ........................................................................ 31Figure17:MainfunctionofPortfolioOptimization ................................................................. 32Figure18:Finaloutputofoptimizedweights ........................................................................... 32Figure19:Graphofvariableimportance .................................................................................. 33

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Table of Tables Table1:Modelinputs,outputs,succinctdescriptionsandresearchsources .......................... 9Table2:ListofThirdPartytoolsandpackages ........................................................................ 12Table3:ListofCollaborationTools ........................................................................................... 12Table4:SnippetofOutputCSVforWorldTradeModel .......................................................... 28Table5:TheinputdatafortheCobb-DouglasProductionFunctionModel ........................... 29Table6:InputDatasetofAssets’ReturnValues ....................................................................... 30

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Executive Summary

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1. Frontiers in Russian Economics Studies Accordingtoresearchdonebythe Center for Economic and Financial Research (a

Moscow-based independent think tank), enhancing human capital through education reform is one of the keys to unlocking the potential of the Russian economy. The Organization for Economic Co-Operation and Development (OECD) has even coined a term called “knowledge-based economy” based off of the idea that knowledge and technology foster more economic growth and expansion (OECD, 1996). Buildingaknowledgeandunderstandingofeconomicsrequireshugeamountofresearchinvariousformats--fromelectronicresourcestoconventionaltextsorresearchpapers(Acton,2013).Inthepast,researchers,students,andprofessorshadtoanalyzeeconomicdatabymanuallyapplyingeconomicmodelstoanticipateissuesandproblemsthatcanariseintheeconomy(Morey,2016).Theeconomistsmustperformresearchtofindtheapplicableeconomicmodelforthesituationtheyareanalyzing,andthenconvertthemodelintosomesortofprogram(orevencodeitthemselvesifaprogramdoesnotyetexist)inordertohandlebiggerdatasets.Thisprocessisextremelytediousand,ifdonemanually,virtuallyimpossibletoapplytolargedatasets.

Previousattemptsatmakingeconomicmodelsreadilyavailableforresearchers,professors,governments,andstudentsalikehavebeenmostlyimplementedusingmanualclassicalmethods.Thosemethodscanbedefinedastextbooks,researchpapers,articles,andlectures.However,associetyhasbecomemoredigitized,therearenowonlinetutorialsandforumsthatfosterdiscussiononeconomics.Theseattemptsarewidespreadanduncentralized,andtoutilizethesetoolsasalearningmodulewouldrequiresomesortoforganizationanddatastorage.Thereisnocentralrepositoryforthisspecifickindofinformation.Alibraryoronlinedatabasemayhavevarioustextsoneconomicmodels,butsimpleaccesstothemodelsthemselvesstillrequiressiftingthroughvarioussources,whichconsumesresearchtime.

TherearealsomanydifferentsoftwareandprogramminglanguagesthatalloweconomiststoperformthesameresearchandBigDataanalysis.Themaincaveatwiththesesoftwareandprogramminglanguagesisthatthesoftwareistypicallyunavailableoutsideofcorporateoruniversitysettings,andprogramminglanguagescantakeuptomonthstomaster.Whenthemainpointofresearchistoperformdataanalysis,codingthemodelsbecomesanecessaryevil.Simplyput,thereiscurrentlynoeasywaytocutoutthis“middleman”ofeconomicmodeling.

OurprojectwiththeFinancialUniversityundertheGovernmentoftheRussianFederation(FinancialUniversity)aimedtoaddressthisconcernandforgethisfrontier.TheFinancialUniversityisseekingtofinditsplaceinthedigitizedworldthatthisanalysisandresearchismovingtowards.Itsemphasisoneconomiceducationunderscoresthatthereneedstobeauniversity-widedigitalplatformtounifyavastnetworkofprofessors,researchers,andstudents,andfacilitateexpeditedanalysisoftrendsandbehaviorsintheRussianeconomy.Thiswillallowresearcherstocreatebettereconomicstrategiesthatwillfosterabroaderunderstandingoftheeconomytoallowforfutureimprovementsandcorrections.Furthermore,thefutureofthisplatformwillallowtheFinancialUniversitytoseewherestudents’mainareasofinterestamongeconomicresearchtopicslies,andscope

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outtop-performingstudentsforpotentialscholarships,grantsorjobopportunities.Thegoalofthisprojectwastocreatethetooltoassistwitheconomicresearchandeducation. Toachieveourgoal,wesetouttocompletethreemainobjectives:

1. Identifymajoreconomicmodelsthatcoverawiderangeofeconomicfields;2. Programthemodelssuchthatstudentsandresearcherscaninputtheirowndata

andmanipulatethemodelsforanalysis;3. Createaframeworkforaninteractivewebsitethatimplementsthemodelingsystem

thisprojectseekstofacilitate.Throughthisimplementation,userswillbeabletomaximizetheirresearch

productivityandminimizetheamountoftimespentonmundane,repetitivetasksassociatedwithinputtingcompiledeconomicdataintoeconomicmodels.Inthenextchapter,wewillreviewsomebackgroundinformationonthecurrentstateoftheRussianeconomytofurtherclarifythesignificanceofthisproject.Wewillalsouncoverthepastandcurrenttoolseconomistsusetoperformtheirresearch,andpavethewayforthefinalplatformweaimtocreate.

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2. Economic Models: Scope, Significance, and Applications

InthischapterwewillprovideabriefbackgroundtothepastandcurrentstatusoftheRussianeconomyandsummarizecurrenteconomictrends.WewillthenexplainwhytheRussianeconomyisinneedofbettereconomicanalysisforeducationalandresearchpurposes.Thiswillprovidearationaleforwhyoursponsor,theFinancialUniversity,hasrequestedthisresearchtobedone.Thiswillbefollowedbyexamplesandexplanationsofrepositoriesandtoolsforeconomicanalysis,bothpastandpresent. The Russian economy’s unique path and need for better modeling

Russia’seconomicprogresshasbeennotablyslowerthanmostcountriesexperiencingatransitionofpower(Berglöfetal.,2003),anditspathhasbeeninmanywaysuniqueanddifficulttopredict.AfterthefalloftheSovietUnionin1991,Russiastruggledtoregainitsfootingandwasforcedtobeginitsrebuildingduringaperiodofeconomicrecession.

Whilethisisnottransitioncountries,Russia’srecessionlastedmuchlongerandwasworsethanothertransitioningeconomies(Kolodko,2000).InFigure1,itisclearthatRussiaspentmoretimeinarecessionthanmostothercountriesexperiencingatransitionofpower.Furthermore,anotherindicatorofRussia’seconomicperformancecanbeseenwithitslowGrossDomesticProduct(GDP).ByconventionGDPistypicallyusedtodeterminethehealthofaneconomy(Berglöfetal.,2003).

Figure1:TimespentbyEasternEuropeancountriesinatransitionalrecessionafterthe

falloftheSovietUnion(inyears).

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Figure2belowdisplaystheinstabilityofRussia’sGDP.Asacountrysittingonapproximately30%oftheplanet’snaturalresources,theRussianeconomyshouldbeoutperformingmostcountriesduetothisgeographicadvantage(Korabik,1997).However,becauseRussia’sGDPreliessoheavilyonoilprices,theeconomyremainsextremelysensitivetothechangingvalueofoil.

Figure2:Russia'scurrentGrossDomesticProduct(GDP)

TheRussianeconomyhasseenslightimprovementtowards2016,albeitatastrikinglylowrate.Historicallylowoilpricesandinternationalsanctionshavepreventedtheeconomyfrommakingastrongrecovery.AccordingtodatacollectedbyBloomberg,theGDPdeclined0.225%in2016,sinceitpulledoutoftherecessionin2016(Andrianova,2017).ThiscanbeseenbelowinFigure3.

Figure3:Russia'scurrentGDPgrowth.

Thisslowgrowthiscauseforconcern,especiallyifthepriceofoilcontinuestodropbelowasustainablevalue.DuetothenatureoftheRussianeconomy,thereneedstobemoreanalysisandresearchdoneinordertounderstandthemechanicsoftheeconomyandbuildnewtechniquestoimprovegrowthandsustainprosperity.

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How does education and research support economic growth? WilliamWalstad,theDirectoroftheNationalCenterforResearchinEconomic

Education,statedthat“nationsbenefitfromhavinganeconomicallyliteratepopulationbecauseitimprovesthepublic'sabilitytocomprehendandevaluatecriticalissues”(Walstad,1998).Insummary,increasedeconomicliteracyhasthepotentialtoimproveeconomicperformancebecauseitenablescitizenstomakesmarterdecisionsregardinginvestments.Byunderstandinghowtheeconomyworks,acitizencanmakeeducateddecisions,whichleadstoanincreasedreturnoninvestments.Furthermore,themoreresearchthatisdoneontheRussianeconomy,themorepossiblesolutionsandpreventativemethodscanbecreatedtomitigateeconomiccrisissituations.Forexample,ifthepriceofoilsuddenlydrops,aninformedcitizenwouldbeabletounderstandthattheRussianGDPwouldbenegativelyaffectedasaresult,andwouldmanagehisorherassetsaccordingly. What are the current methods of economic study?

Economistscurrentlyusedirectresearchandhardcodingtoconducteconomicanalyses(Pavlov,4April,2017).Economistsmanuallysearchthrougheconomicliterature,notes,journals,and/oronlinesourcesinordertofindtheapplicablemodelsandformulas.Thentheyhavetomanuallyhardcodesaidmodelsandformulasintotheprogramoftheirchoice.Economistswastevaluabletimeperformingthisresearchastheyaretryingtosolveaproblemoranalyzesomeeconomicphenomenon.Forhigher-leveleconomicmodels,thereisaplethoraoffactorsthatrequiresomeoneproficientinaprogramminglanguageorproprietarysoftwaretofullycapture.Economistsarefacedwithadecision:eitherlearnhowtodoitthemselves,whichtakesasignificantamountoftime,orcontractanexternaldevelopertotacklethetechnicalaspectsofthemodel. What are economic models? Economicmodelsareoneofthebestwaystolearnabouteconomicbehavior.Simplyput,theyaretheoreticalconstructsusedtoexplaincurrentbehaviorandpredictfuturebehaviorofeconomicsystems(Morey,2016;Mankiw,2001).Theyareessentialtoeconomictheory.Theyallowyoutounderstandhowandwhyspecificeconomicbehaviororphenomenaoccur(SamuelsonandNordhouse,1998).Thesemodelscantakemanyshapes--frombasicgraphstocomplexequations.Theyarecreatedbycalculatingtherelationshipbetweenvariablesinasystem--aswellastakingintoaccountthepastandpresentbehaviorofthesystemundervaryingcircumstances--tocreateatheoreticalgeneralizationabouthowthesystembehaves.

Throughtheuseofeconomicmodels,economistsgaininsightintotheinnerworkingsofasystem.Theyallowtheusertopeelbackhiddenlayersandgraspthemorefine-tuneddetailsthatmaynotbeexplicitlyvisible,andpavethewayforamorewell-roundedknowledgeofthesystem.Themoreknowledgeonehasofasystem,themoreroomthereisforunderstandinghowtoimproveit.Onalargerscale,onecanuseeconomicmodelstostudyactualeconomiesofentirecountries.Theycandeterminehowvaluablearesourcemaybe,ortoanalyzeriskfactorsofpoliticalmoves.Thisallowsonetoestimatetheoutcomeofpotentialdecisionswithouttheriskofactuallymakingthem.Indoingso,

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onecanseethecollateraleffectsofadecision,maximizingtheconservationoftime,money,andevenlives. Why are models important?

Toemphasizetheimportanceandimpactofeconomicmodeling,letuslookatanexampleofastudydonebyresearchersattheUniversityofSouthernCalifornia’sKeckSchoolofMedicineandTruvenHealthAnalyticsinCambridge,Massachusetts(Johnson,2015).Aneconomicmodelwascreatedtodeterminethebenefitsofco-testingforcervicalcancerandhumanpapillomavirus(HPV).Themodelconcludedthatbycombiningthetwotests,doctorswouldbeabletodetectupto150,000additionalcasesofcervicalcancer,prevent100,000deaths,andsave$4.4billionovera40-yeartimeperiod.

Economicmodelsvaryfromcasetocase,buttheirapplicationsmayalsobeusefulinotherscenarios.InastudydonebyGibbardandVarian(1978),theresearchersconcludedthattheamountofvarianceamongeconomicmodelscanbeverysmall,anditisdifficulttodistinguishbetweenthedifferenttypesofmodels.Thisisduetothefactthateconomicmodelscanrepresentmanyphenomenaandsituations,withverysmallvariationamongthem.Thismakesitdifficultforeconomiststofindthemodelmostapplicabletotheproblemathand.TheFinancialUniversityislookingforawaytoorganizeandstoreimportanteconomicmodelsinacentralizedlocation--arepository--toexpeditetimespentresearchingforitsstudentsandforothereconomicresearchers.Bydoingso,thestudentsandresearchersoftheFinancialUniversitywillbeabletogetstraighttotheirresearch,ratherthanwastingtimesearchingandcodingthemodelstheyneedtoperformtheiranalysis. How will these models be accessed by university staff and students? Arepositoryisacentralizedlocationinwhichdataarestoredandfromwhichtheycanbeaccessed(OberkamfandRoy,2010).Therefore,themodelsshouldbehousedinasinglerepositorytocreatea“one-stop-shop”foreconomiststousetocarryouttheirresearch. Thegoalofarepositoryistocutdownthetimewastedontrivialsteps,suchascoding,inordertoalloweconomiststogaintheinformationtheyneedquickly.

Witharepositoryofeconomicmodels,economistscouldefficientlysearchthroughthedifferentmodelstofindtheonemostapplicabletosolvetheproblem.Previously,GibbardandVarian(1978)categorizedeconomicmodelstheystudiedinaresearchpaperdependingonthetypeofcalculationperformed.Thisallowedthereadertofullyunderstandhowacertaintypeofmodelwithinaspecificcategorycanbeappliedtoreal-worldsituations.Thesameresultscouldbeachievedbyimplementingarepositoryoffullyfunctioningeconomicmodelsthatarereadyforuse.Forexample,ifeconomistswerelookingforaneconomicmodelforportfoliooptimization,theycouldsimplysearchforoptimizationmodelsandeasilylocatethedesiredone. Thevalueofarepositoryofeconomicmodelscanbebetterunderstoodbystudyingapreviousimplementation.In2012,astudywasdonebytheUniversityofWarwickinwhichtheyanalyzedbusinessmodelsandbusinesscasesappropriatetosupplychaindata(Cave,2012).Theystoredthisdatainarepository,whichallowedstakeholderstoaccesstheinformationeasily,andassistedthemindesigningsuitablebusinessmodelstoenhancethevalueoftheirexistingrelationships.Forexample,ifastakeholderwantedtocreatea

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cost-benefitanalysisofpurchasinganewmachinedesignedtomaximizeproductionwhileminimizingtime,theycouldsimplygointothisdatabaseandsearchforthebusinessmodelforthissituation.Then,theycouldinputtheircompany’sdataandstatisticstoseehowbeneficialthisbusinessmovecouldbeinthelongrun.Figure4belowprovidesavisualizationofthemechanicsofsucharepository.

Figure4:Mechanicsofarepository

What are current methods used to study and analyze economic models? ThereareseveraltoolseconomistsinRussiacancurrentlyutilizetointeractwith

theseeconomicmodels.SoftwarelikeMatLab,Maple,andStataorprogramminglanguageslikeR,Python,andC/C++canallbeusedtocodeeconomicmodelsusingvariablesthattheusercanchangeaccordingtodatatheyneedtoanalyze(Dr.OlegPavlov,personalcommunication,April19,2017).Thereareeventutorialsonlineonhowtousethesesoftwareprogramsandprogramminglanguagestocodeeconomicmodels(SargentandStachurski,2014).However,whilethesetoolsareallextremelyusefulandapplicabletostudyingeconomicmodels,theyarenotthetoolsofaneconomist,butratherofacomputerscientist.Theissueisthatonemustbeproficientinusingthesetoolsinordertoapplythemtoeconomicmodelingandanalysis.Aresearchermustgothroughsomesortof“middle-man”beforeperformingtheiranalysisusingthesetools.Inmorecomplexcases,aresearchermayneedtoevenhireaprofessionalsoftwareengineer(sometimesevenateamofengineers),ormaybeattendaclassonhowtousecertainsoftware.Economistswouldprefertonotdealwiththistypeofwork,buttodosotheywillneedotherstohelpthemtobeabletofocussimplyonapplyingeconomicmodelsusingdatathattheyorothershavecollectedtoanalyzetheeconomictrendsandmakepredictions.

Inthenextchapter,wewillexplainthemethodswewillapplytocarryoutourresearchinordertoachieveourgoalofcreatingaplatformforFinancialUniversitystudentstohaveeasyaccesstoeconomicmodelstoenhancetheireducationandfostertheirgrowthinthefield.

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3. Methodology: Program Fabrication Thescopeofthisprojectwastocreateasystemthatwouldactasarepositoryforeconomicmodels.Afterconsultationwithouron-siteliaisonProfessorAntonLosev,wedeterminedwhatwasfeasibletoaccomplishwithinourtimehere.Weassessedourgoalsinrelationtothetimeframeweweregiventoachievethecreationoftheplatform,andconcludedthatwewouldtackleonlyseveralessentialaspectsoftheoveralltasktobeabletodeliverqualitywork. Theseareourobjectives:

1. Identifymajoreconomicmodelsthatcoverawiderangeofeconomicfields;2. Programthemodelssuchthatstudentsandresearcherscaninputtheirowndata

andmanipulatethemodelsforanalysis;3. Createaframeworkforaninteractivewebsitethatimplementstheeducational

modelingsystemthisprojectseekstofacilitate.

Bydoingso,wehopetoprovidetheFinancialUniversitywithatooltoallowforeasieraccesstoeconomicdataandfacilitateresearchontheRussianeconomy.Thistoolcanbefurtheradaptedforuseinthebusiness/consultingfield. Economic Model Research Afterweassessedourgoals,weconcludedthatthereshouldbefourstartingmodels:WorldTrade,Cobb-Douglas,LoanScoring,andPortfolioOptimizationmodels.Thesemodels,proposedbyoursponsor,exemplifythediversityofanalysismethodsthereareintherealworld.Byensuringthisdiversityexistsintherepository,studentswhousetherepositorywillbemorewell-roundedwhenproblemsolving.Themodelscoverthemainaspectsofeconomicmodeling:optimization,scoring,ranking,anddataassessment.Additionally,eachmodelhasthreemaincomponentsthatareessentialtotheeducationalvalueofthisrepository:inputdata,modelalgorithm,andmodeldescription. Thefirsttaskwetackledwasdatacollection.Wedecidedthatthedataforeachmodelshouldcomefrompublicsources.Thiswasdeterminedbecausepublicdataisthemostaccessiblesourceforthetargetusersofthissystem,whoarestudentsandresearchers.Whilediscussingthiswithoursponsor,welearnedthatuserscaneasilypulldatafromawebsitecalledKaggle.ThiswebsiteisessentiallyahubforBigData--ithostscompetitionsondataanalysisandprovidesfreedownloadsoflargedatasetsasCommaSeparatedValuefiles,or‘.csv’files,whichisthestandardfiletypeforinputsinthisrepository.Furthermore,thedatasetsareoftenextremelyspecialized.Youcanfindanythingfrommovielists,stockexchangehistory,andeventrendingYouTubevideos.Thisallowsuserstoapplythemodelstoanytopictheymayhaveinterestin.

Next,wehadtoactuallycodetheeconomicmodels.Intermsofthealgorithmconstruction,wedeterminedthattheeasiestlanguagetousewasPython.Thiswasdeterminedforavarietyofreasons.Pythonfollowsastrictpactof“Thereshouldbeone--andpreferablyonlyone--obviouswaytodoit.”(Peters,2004).WefeltthatthisaspectofPythonisessentialforcollaborationwithourRussianteammates,whowerefamiliarwiththisprogramminglanguage.Pythonalsohasthelargestthirdpartyselectionfordata

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sciencepackages.Thiswillgreatlyimprovetheperformanceofthealgorithms,asthesethirdpartypackagesareextremelypowerfulandwillensureefficientandaccurateresultswhilecuttingdownonprogrammingtime.CompetitionforPythonisnotevidentfordatascience.Infact,onlytheRprogramminglanguageiscomparabletoPythonforstatisticalanddataanalysisusage.WechosePythonoverRsincethelatterlanguagecannotbeeasilyintegratedintoawebsite'sframework,whilePythoncan.Duetoitswidespreadinternationaluse--thereisanabundanceofframeworksspecificallydesignedforwebdevelopment.

ThelastmaincomponentofourplatformisthedescriptionWiki.Thisisthedocumentationthatuserscanuseasguidelinesforthesoftwareandmodelsthatwedesign.Theyexplainthesignificanceofthedata,thealgorithmstructure,theoutputstructureandexplanationastowhatausercandowiththeresultsofthealgorithm.Togainanunderstandingofthemodelsourselves,wehadtoconductextensiveresearchonthemodelstounderstandtheparameters,calculationsandexpectedresults.Thisresearchwasthenputintotextalongsidethecorrespondingcodesothatuserscanseetheconnectionbetweenthedatamanipulationandthemodelalgorithm.Tofurtherenhancetheeducationalvalue,weensuredthatthecodecontainedmanyvisualizationsandgraphicalrepresentationsofthedatamanipulationtoprovideforamorewell-roundeduserexperience.Thiscomponentisessentialtotheeducationalaspectoftherepository–byreadingthemodeldescriptions,userscanactuallygainanunderstandingofhowthemodelworksbeforeusingthealgorithmstoapplytotheirowndataforanalysis. Programming the models Tobeginthecodingprocessforeachmodel,wefirsthadtodeterminetheinputandoutputparameters,aswellasthedescriptionofwhatthemodelwillactuallycompute.Thiswillshedlightontheeducationalvalueofstudyingthemodel,asseeninTable1below.

Table1:Modelinputs,outputs,succinctdescriptionsandresearchsources

Model Input Output Description Source

World Trade Trade and GDP

Datasets

CSV file with ranked countries & command

line query interface

Model shows what inputs need to be altered for a desired output. This

also graphically shows the relationship between parameters

Mikic and Gilbert (2009)

Cobb- Douglas

Dynamic dataset

Graphic representation of relationships

Model shows what inputs need to be altered for a desired output. This

also graphically shows the relationship between parameters

Stewart (2008)

Portfolio Optimization

CSV file of stock

portfolio returns

Optimized weights for each asset for

maximum return

Model shows the relationship between investments.

Sharpe, Alexander and Bailey (1999)

Scoring Credit history dataset

Binary predictions of whether customers

should receive a loan.

Model allows the user to gain a better understanding of machine

learning techniques.

Gadil (2016)

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AmorerobustexplanationoftheinnerworkingsofeachmodelcanbeseeninAppendixD. Organizing and storing the models

Westoredthemodelsinanetworkdatabasewhereallthealgorithmsforthevariousmodelswouldbeaccessible.Weorganizedtheselectedeconomicmodelsintofolderscorrespondingtotheirpracticaluse.Forexample,producingcalculationsorperformingdataanalysis.Withinthosefolders,theywillbesubdividedintothecategoriesinwhichtheyoperate--forexample,optimizationorrankingparameterswithinlargedatasets.Bydoingso,weexpeditetheeconomist’sresearchandallowthemtogetstraighttotheanalysis. Front-end framework Thefront-endofthisprojectwouldideallybeaccessibletoalargeaudience.Sincethemainideaofthisplatformistoprovideatoolforresearchandeducationlimitingtheaccessibilityoftheplatformwouldessentiallynegatetheimpactwehopetoachieve.Thisinitselflimitsthedevelopmentprocessofthedeliverabletospecificmethods.ThefirstistomakeanexecutablepackageforWindowsbasedsystems.However,hislimitstheaudiencesincethiswouldnotbecompatibleforMacintoshcomputers.Thesecondavenuewouldbetomakeaweb-basedtool.Thiswouldbetheidealsolutionsinceweb-basedtoolsareaccessiblefromallmoderndevices,andcanshiftthebalanceoftheprocessingfromtheuserclienttotheserver.

Wecannowdeterminethestructureofthewebsitebasedontherequirementsforthesystemasawhole.Thetwopossibilitiesthatcanbeexploredinthistimeframeiseithermakingalocalwebsitethatthefinancialuniversitywillhost,ormakingapagethatwouldbehostedbytheFinancialUniversityfittedwithdescriptions,Pythoncode,andalgorithms. Thefirstoptionthatcouldbeexploredwouldallowanyuser(whetheritbestudent,teacher,orcompany)toruncodewithoutdownloadinganyfileslocally.Thiswouldalleviatetheprocessingrequirementsontheclientendofthesystem,andmakeitaccessiblefromanydevicethatcanconnecttotheuniversityoranyotherhostinglocation.Accessthroughtheuniversitywillrequirestudentsandteacherstologinwiththeiruniversitycredentialstoaccesstheplatform.Tobroadentheplatform’soutreach,itcouldbehostedonapublicwebsite.Thiswouldallowpeoplewithoutcredentialstoaccesstheplatform.Thesecondoptionistohaveafrontendstructurethathasadetaileddescriptionofthemodel,howitworks,howtorunit,andwhatdatacanbefedintoit.Thispagewouldalsoentailthedata,code,andnecessarythirdpartypackages.Thispossibilitycomeswiththeassumptionthattheuserhaspreviousknowledgeorguidanceavailableaboutrunningtheprogramlocally. Sincewehonedourfocustodevelopingthemodelscorrectly,wedecidedtousethesecondoptionforthefrontendsinceitislesstimeconsuming.Thefirstoptioncouldstillbeachievableinthefuture,andwouldallowtheuserstohaveanenvironmentthatwouldencapsulatetheprocessofdevelopingamodel,testingamodel,andsharingamodelwithemployersorteachers.

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Survey Development Allofourobjectivesliewithintheunderstandingthatthistoolwillbecomeanintegralpartofeconomicseducation.Todefendourassumption,wesentoutasurvey,throughatoolcalledMicrosoftForms,inordertogatherresultsrapidlyinanorganizedfashion.Sincewewantedthesurveytobefast,easy,andaccessible,wefabricatedsevenquestionsthattouchonbackground,appealofthetool,andwaysthetoolshouldbeused.Figure5showsthelistofquestions,alongwiththesectionsthatcorrespondtoitssignificance.

Figure5:Surveyquestions

Third Party Tools and Packages Toaccomplishourgoalsofcreatingefficientandaccurateprogramsforeachmodel,weneededtousefourspecificthirdpartytools.Thesetoolsarepackagesthataredesignedtoeasilyparse,manipulate,andanalyzeBigData,andareexplainedinTable2below.Thesepackagesareextremelypowerfulandallowustoperformhigh-levelcalculationsonlargedatasets.

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Table2:ListofThirdPartytoolsandpackages

Package/Tool Name

Application Description

Pandas Big Data analysis in Python

Allows data to be easily manipulated

NumPy Big Data analysis in Python

Allows mathematical operations to be easily run

Spyder Python IDE Streamlines programming process by catching bugs quicker

CvxOpt Data optimization in Python

Allows convex optimization application straightforward

Aspreviouslymentioned,allaspectsofthisprojectuseopensourceresourcesin

ordertoachievethegoalssetout.Thesethirdpartypackagesstreamlinedtheprocessesweneededtousetoachieveourtechnicalgoals.Thiscomeswiththerequirementthattheenduserwouldalsohavetodownloadthesepackageswiththecodeattachedtoeachmodel.Wewillbebundlingthecodewiththethirdpartypackagesinordertomaketheprocessfortheusertousethealgorithmseasier.Itisimportanttonotethatwearenotviolatinganytrademarks,copyrights,orpatentssinceeverythingisopensource.

Intermsofcollaboration,weuseddiverseoftoolstosharecode,workondocumentstogether,communicate,sendoutsurveys,andtrackourprogress.Table3explainseachtool'ssignificance.

Table3:ListofCollaborationTools

Package/Tool Name

Application Description

GitHub Version Control Allowed the team to effectively share code Google Drive Document Storage and

Fabrication Allowed the team to work on document and

presentations simultaneously. Telegram Communication Enabled the team to effectively communicate over

laptops and mobile devices Trello Organization Allowed the team to effectively track progress, set

tasks, and stay on top of deadlines Thegoalsetbyoursponsorwasextremelychallenging,butwiththesemethodswe

wereabletofostereasycollaborationbetweenusandtheFinancialUniversitystudentstocompleteourobjectives.Inthenextchapter,wewilldiscusstheresultsofourprojectandourgoalsforitsfuture.

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4. Results and Discussion TheresultofourprojectwasthedevelopmentoftheFinancialUniversity’sWikifor

economicmodels.ThewebsiteislocatedontheFinancialUniversity’swebsite.Aftercompletingourobjectives,wecreatedaframeworkthatsetsthefoundationforaplatformthathasthepotentialtobecomeavitalresearchandanalysistoolnotonlyfortheFinancialUniversity,butforeconomistsandeconomicstudentsacrosstheglobe.Ourprojectutilizedourstrengthsindataanalysisandmanipulation.ThisintroducesaFinancialUniversity-specificwayforresearchersandstudentswhoarespecializedinareasotherthandatasciencetoleveragetheirexpertisewithBigData,andproduceapreviouslylaborintensiveresultveryquickly. Survey Results

Theresultsofoursurvey(shownbelow)paralleledourdevelopmentoftheplatform.

Figure 6: Survey results – gauging the audience

ItisimportanttonotethatwedidnotreceiveanyresponsesfromFinancialUniversityfaculty.Becauseofthis,wecannottellifourplatformwillbeofuseasateaching

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toolforprofessorstouseintheclassroom.Wedecidedthatwewouldmoveforwardundertheassumptionthatour30respondentswillbecomeourtargetaudience,asweneededtobegindevelopingourplatform.Thatbeingsaid,wecateredourplatformforamorepersonal(outsideoftheclassroom)educationaluse.Asourimplementationismodular,thistoolcanbefurtherdevelopedtobecomeateachingtoolforprofessorstouse,ifneeded.

OurrespondentsweremostlyundergraduatestudentsoftheFinancialUniversity,whosemainresearchisconductedthroughcurrentsoftwarelikeMicrosoftExcelandAnyLogic.However,themajorityofthesestudentsstatethattheywouldlikeamoreinteractiveeducationalsoftware.InFigure7below,itisclearthatasignificantportionofthestudentssurveyedwouldlikemodeldescriptionsofthemodelstounderstandthetechnicalequationsandhigh-leveltheorybehindthem.

Figure7:Surveyresults–decidingwhattoincludeintherepository

TheseresultssupportedtheformatoftheWikiandgaveusabetterunderstandingofourpotentialaudience.Bycollectingthisdata,wewereabletodeveloptheplatformwiththedesiredmodulesandmodelsforend-users. Creating the framework

Withoursurveyresultsinmind,wecustomizedourplatformtoourtargetdemographic-FinancialUniversityundergraduatestudents.Wedecidedthatwithourtimeconstraints,creatingasimple“Wiki”pageofthemodeldescriptionsandlinkingdownloadablecodeofthemodels(shownbelowinFigure8)wouldbetheeasiestimplementationwecouldsuccessfullyimplement.

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Figure8:CurrentimplementationoftheWiki

ThisWikipageisontheFinancialUniversity’swebsitesoitiseasilyaccessibletostudents.TheWikipageincludesathoroughdescriptionofthemodel,theformulasneededformanualcalculation,andthedownloadlinksforthefilesholdingthemodelalgorithms.Thisallowsstudentstogainanunderstandingofthetheorybehindthemodels(throughthedescriptions),andanalyzethelogicofthemodels(throughtheheavilydocumentedalgorithms).Furthermore,asthestudentscandownloadthealgorithmsfortheirownuse,theyareabletoinputandviewresultsontheirowndata.

ThemodularframeworkoftheWikialsoallowsforfuturedevelopmentofthisprojectthroughaweb-basedinterface.Notonlydoestheframeworkmakeourcodeeasiertoimplementinthefuture,italsoprovidesatemplateforaddingnewmodels.Thistemplatewilloffertheabilitytoseamlesslyintegratemoremodelsinconsistentform,providingascalabilityfactortoourproject.However,thedevelopmentofanywebapplicationisboundtohavelimitations. Study Limitations

ThefirsttwoweeksofourtimeattheFinancialUniversitywasessentiallyspentdiscussingwithoursponsor,andourRussianteammates,whattheend-goalofourprojectwas.Theideaoftherepositoryhadbeensetinstonesincethebeginningofthisproject,butwehadtodeterminehowtoshapethefinalplatformandWiki.Wealsohadtodiscusswhatwasfeasibletocompleteintheremainingweeks.Onlyafterwesuccessfullyidentifiedthesetwogoals,wecouldbeginourwork.

Furthermore,asweareateamofcomputerscience,roboticsengineering,andindustrialengineeringmajors,ourknowledgeofeconomicsandeconomicmodelswasextremelylimited.Foralltheeconomicmodels,wehadtospendweeksperformingresearchwithourRussiancounterpartsinordertoensurethatouralgorithmswere

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accurate,andthateachstepwasheavilydocumentedtoensuretheeducationalvalueoftherepositoryremainedintact.

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5. Conclusion and Recommendations

Throughthedevelopmentandimplementationofoureconomicmodelrepository,webelievethatbotheconomicsstudentsandresearchersalikewillbeabletoexperiencethesignificantsimplificationofthedatascienceaspectofeconomicsresearch.Wehavefoundthattheincludedmodelswillaideawiderangeofproblemsthatstudentsandresearchersface.Whiletherearealternativestoourplatform,suchastheRprogramminglanguageandproprietarysoftwaresuites,theyrequiretheusertobecomeproficientineitheraprogramminglanguageoracommercialpieceofsoftware,whichcanbedifficulttolearnfromscratch.

Bycreatingcodetomanipulatelargeamountsofspecificdatatoobtainclearlydefinedresults,wehavearmedeconomistsandstudentswithapowerfultoolthatcantaketheirresearchtothenextlevel.Thedatawecollectedthroughoursurveyhelpedusdevisetheoptimalwaytodeliverthisplatformtotheclientsthatwillhaveaccesstoit.Additionally,theworkwehavedonehasbeenperformedwithfuturedevelopmentoftheplatforminmind.Futuredevelopmentswillneedtoincludetheadditionofothermodels,thedevelopmentofapracticalfront-enddashboard,andtheabilitytoinputthird-partydatasetsthatcaninteractwiththemodels.ByloweringthebarriertoentryforstudentsandresearcherstouseBigDatathroughcomprehensivemodeldescriptionsandfunctions,ourgoalistobolsterfinancialliteracyandpromoteeconomicresearchandeducation. Recommendations

Afterweresearched,programmed,andimplementedourrepository,weanticipateandencouragethefollowingfuturedevelopmentsandimprovementsforboththeendusersofourplatformaswellasforsoftwaredevelopers. Forusers: • Werecommendreadingthedocumentation(developedbyus)providedwiththe

models.Whilelearningtheactualcodedefeatsthepurposeofourproject,havingageneralideaofwhatisgoingonwillhelpwiththeoptimalutilizationofthemodels.

Fordevelopers: • Back-testingonrealmarketdataisrecommended,assomemodelswereonlytestedon

ourowncollecteddata.• Whenaddingadditionalmodels,werecommendtocreateseparatefilesforeachmodel,

andutilizepre-existingdatamanipulationpackages,suchaspandas,NumPy,andsklearn,thatweincorporatedintoourmodels.Thiswillnotonlymakemanycomplicateddatamanagementtaskseasier,butalsoallowformodelstosharerobust,provencode,minimizingtheneedforrepetitivecode.

• Whendealingwithlargequantitiesofdata,theamountoftimeittakestoactuallyruntheprogramcanbesubstantial.Keepthetimeittakestocalculatetheresultsinmindwhenincorporatingnewfeaturesormodels.Ifthecalculationsaredoneonacentralizedserverandtheresultsaredeliveredthroughawebapporothermedium,runningcalculationsformultipleusersmaybogdownperformanceandmakethe

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platformunusable.Thiscanberectifiedbydevelopingback-end,loadmanagementcode.

• WhileusingthePythonconsoleisbetterthaneditingthecodeyourself,amoreuser-friendlyuserinterfacewouldfurtherbroadentheuserbaseofthisplatformaswellasmaketheutilizationofsuchaplatformlessintimidatingtoanewuser.Thiswouldalsominimizetheusererrorbyprovidinglimitedvalueinputs.

• Ifthecodeiscentralizedtooneserverandaccessedthroughawebapp,werecommendtheuserinputbesanitized.Thiswillpreventmaliciouscodefrombeingintroducedintothemodelcode,preventingintentionalorunintentionalcorruptionofthefiles.Inthecaseofmodelsthatusesuchmethodsasmachinelearning,thecodeneedstolearnfrommultipleiterationswhatisgood,andifthecodewasresettoitsinitialstateaftereveryiteration,thisnewlearningwouldbelost.

Duringthenextstepsoftheproject,werecommendthatthefocusbeonintegrating

therepositoryintoacentralizedserverthatcanbeaccessedthroughawebapp.Thisisthenextlargeportionofthefinalplatform,andbecauseofthis,itneedsasignificantinvestmentoftimeandresourcestocomplete.TheadditionofmoremodelstotheWiki,whilenotbeingeasy,canbeaccomplishedincrementallynowthattheplatformstructureissetup.

Tosummarize,werecommendthenextstepindevelopmentofthisprojectshouldbetofocusondevelopingawebappthatallowsuserstonotbedependentonhavingtodownloadanycodeordataset,butratherinteractwithauserinterfacethatallowssimilardatainputandoutput.Thisrepositorywascreatedwiththeintentionofalleviatingtheextraeffortstudentsandresearchersmustputinwhenperformingeconomicsresearchandanalysis.WehopefuturedevelopmentofthisrepositoryleadstotheFinancialUniversitygainingauniqueandspecifictooltohelpboosttheirstudentswork.Bydoingso,wehopethatourplatformachievesouroverallgoalofprovidinguserswithatooltotrackandanalyzetheRussianeconomytogainabetterunderstandingofit.

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• Acton,Q.(2013).IssuesinGeneralEconomicResearchandApplication.Georgia,Atlanta:ScholarlyEditions

• Andrianova,A.(2017,May17).RussianRecoverySputtersasEconomyContinuesSlogAfterCrisis.RetrievedSeptember15,2017,from

• BalatskiiE.V.andKonyshevV.A.(2006),Russianmodeloftheeconomy'spublicsector.London,England:Springer.

• Berglöf,E.,Kunov,A.,Shvets,J.,&Yudaeva,K.(2003).TheNewPoliticalEconomyofRussia.Cambridge,MA:MITPress.

• Cave,Jonathan,BusinessandEconomicModelsforSupplyChainDataRepositoryServices(July30,2012).RetrievedApril18,2017,fromSSRN:https://ssrn.com/abstract=2141913orhttp://dx.doi.org/10.2139/ssrn.2141913

• Few,S.(2005).DashboardDesign:BeyondMeters,Gauges,andTrafficLights.BusinessIntelligenceJournal,10(1),18-24.

• FinancialUniversityundertheGovernmentoftheRussianFederation(2001).WelcometoFinancialUniversity.Retrievedfromhttp://international.fa.ru/Pages/Home.aspx.

• Gibbard,A.,&Varian,H.(1978).EconomicModels.TheJournalofPhilosophy,75(11),664-677.Retrievedfromhttp://www.jstor.org/stable/2025484

• Jacobs,J.R.B.(2014).SecurityUsingDataAnalysis,Visualization,andDashboards.Somerset:JohnWiley&Sons,Incorporated.Retrievedfromhttp://ebookcentral.proquest.com.ezproxy.wpi.edu/lib/wpi/detail.action?docID=1599321

• Johnson,M.(2015,November18).EconomicModelSaysCervicalCancerCo-TestingwithmRNA-BasedMDxCouldSaveLivesandMoney.RetrievedApril15,2017,fromhttps://www.genomeweb.com/pcr/economic-model-says-cervical-cancer-co-testing-mrna-based-mdx-could-save-lives-and-money

• Kołodko,G.W.(2002).Globalizationandcatching-upintransitioneconomies.Rochester,NY:Univ.ofRochesterPress.

• Korabik,K.M.(1997,December1).Russia'sNaturalResourcesandtheirEconomicEffects[Scholarlyproject].InPennsylvaniaStateUniversityCollegeofEarthandMineralSciences.RetrievedApril21,2017,fromhttp://www.ems.psu.edu/~williams/russia.htm

• Kudrin,A.,&Gurvich,E.(2015).AnewgrowthmodelfortheRussianeconomy.RussianJournalofEconomics,1(1),30-54.

• Lee,J.C.(2016,April12).WhytheRussianEconomyIsTumbling.TheNewYorkTimes.RetrievedApril21,2017,fromhttps://www.nytimes.com/interactive/2016/04/12/world/europe/russian-economy-tumbling.html?_r=0

• Mankiw,N.G.(2001).PrinciplesofEconomics(2nded.),Boston,MA:HarcourtCollegePublishers.

• Monroe,J.(2016,March29).DesktopEconomicDashboardUsingR.RetrievedApril21,2017,fromhttps://monroestatistics.wordpress.com/2016/03/18/desktop-economic-dashboard-using-r/

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• Morey,E.R.(2010).AnIntroductiontoEconomicModels.Boulder,CO:UniversityofColorado,Boulder.

• Oberkampf,W.L.,Roy,C.J.,&SafariBooksOnline.(2010).VerificationandvalidationinScientificComputing(1sted.).NewYork:CambridgeUniversityPress.

• Ouliaris,S.(2012).Finance&Development.RetrievedApril18,2017,fromhttp://www.imf.org/external/pubs/ft/fandd/basics/models.htm

• Rundensteiner,E.(2017,April14).Personalinterview.• Saeed,K.(2017,April14).Personalinterview.• Saeed,K.(2016).DevelopmentPlanningandPolicyDesign(2edition).England:

AveburyBooks.• Samuelson,PaulA.,andNordhaus,WilliamD..(1998).Economics.Boston,The

McGraw-HillCompanies,Inc.,Chapter1,(pages3-7).• Sargent,T.J.,&Stachurski,J.(2014,June10).QuantitativeEconomics.Retrieved

April21,2017,fromhttps://lectures.quantecon.org/about_lectures.html• Sharpe,W.F.,Bailey,J.V.,&Alexander,G.J.(1995).Investments.EnglewoodCliffs,

NJ:PrenticeHallInternational• Stewart,J.,Calculus:EarlyTranscendentals.ThomsonBrooks/Cole,6thEdition,

2008,(pages857-887).• TheKnowledge-BasedEconomy.(1996).RetrievedSeptember15,2017,from

https://www.oecd.org/sti/sci-tech/1913021.pdf• Varian,H.(2014).MachineLearningandEconometrics[PDFDocument].Retrieved

fromLectureNotesOnlineWebsite:https://pdfs.semanticscholar.org/fcb1/10653256f8e535bf074403c8847a6a3be76b.pdf.

• Vladimir,C.,&Galina,A.(2016).ProblemsoftheRussianEconomy.MediterraneanJournalofSocialSciences.

• Walstad,W.(1998,December1).Whyit'sImportanttoUnderstandEconomics.RetrievedApril21,2017,fromhttps://www.minneapolisfed.org/publications/the-region/why-its-important-to-understand-economics

• WorldBankGroup.(2017,January1).RussianFederation.RetrievedApril22,2017,fromhttp://data.worldbank.org/country/russian-federation

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Appendices Appendix A: Sponsor Information TheFinancialUniversityundertheGovernmentoftheRussianFederation,openedonMarch2nd,1919,isoneofthetop15Russianuniversities(ExpertRussianRatingsAgency,2016).TheFinancialUniversityisstate-fundedandhoststhirtyeightsubdivisionsthatalllendthemselvestowardsthesuccessoftheirstudents--mainlyineconomicfields.ThemottooftheFinancialUniversityis“Wevaluethepast.Webuiltthefuture.”(FinancialUniversity).Theypridethemselvesontheadvancedlearningtechnologiesandstateoftheartprogramsthathelpmoldhigh-performing,expertgraduates. Initiallystartingwithanenrollmentofaround2,000students,theFinancialUniversityhasgrowntomorethan20timesthatsize.Overthespanofits10campusesand25universitybrancheslocatedthroughoutthecountry,theschoolhasanenrollmentofaround50,000studentsasof2016(FinancialUniversity).HighereducationopportunitiesrangefromBachelorDegreeprogramstoPhDprograms.Underthe21scientificschools,studentscanchoosetostudywithinthefieldsofeconomics,financeandlending,management,law,politicalscience,appliedmathematics,computerscience,andmore.Additionally,theFinancialUniversityalsooffersarangeofresearchopportunities.Theuniversityaimsitseffortstowardsthe“developmentofnewprojects”and“evolvingcreativethinkingoftheteachingstaff”toassemblehigh-levelresearchteamsinordertofurtherdevelopmentofknowledgeabouttheeconomy,finance,security,andcorporategovernment/businessstrategydevelopment.

Figure9:FinancialUniversityDepartmentStructure

InternationalcollaborationisanotherskilltheFinancialUniversityfostersinitsstudents;thefacultyattheuniversityteachtheirstudentshowtoinitiateandpromotedialoguebetweendifferentcountriesandculturesbyofferingarangeofexchange

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programs.Bydoingso,theyhopetoshapewell-roundedandopen-mindedindividuals,whocanthenusethecommunicationandcooperationskillsgainedfromworkinginmulticulturalenvironmentsandapplythemtotheprofessionalworld.

ThisprojectspecificallyhastiestotheDepartmentofDataAnalysis,DecisionMakingTheoryandFinancialTechnology,whichisoneofthe14maineconomicdepartmentsintheuniversity.Thisdivisionspecificallydealswiththedecisionsinbusinessthatusedataanalytics,forecasting,andotherBusinessIntelligencetools.Thebranchleaderanddepartmentheads,Dr.VladimirSolovyev,Dr.IrinaDenezhkina,andDr.VadimFeklin,allhaveexperienceinbusiness,mathematics,andinformationtechnology.Theon-siteliaisonforthisIQP,ProfessorAntonLosev,isanassistantprofessorwithinthisdivision.

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Appendix B: Interview Protocol Before the day of the actual interview:

Ø Email/contacttheintervieweeifwehavepermissiontousetheirname,title,andquotesfromtheinterviewinourprojectproposal.Furthermore,wewillaskifwehaveconsenttouseanaudiorecorderduringtheinterviewtoensurewecanquoteasaccuratelyaspossible.

• Ifwedonothaveclearancetouseanaudiorecorder,oneofourteammemberswillbetakingnotesduringtheinterview.Thesenoteswillbesenttotheintervieweeaftertheinterviewsotheyarefullyawareofthepointsofinformationgathered,andtoallowthemtoconfirmordenyanydirectquotes.

Attheactualinterview: Ø Introduceteammembersandproject/projectgoals.Ø Thanktheintervieweefortakingthetimetomeetwithus.Ø Clarify:Canweusethisinformationasacitationinourdocumentation?Wewillcitethe

dateandtimeofthisinterview,aswellastheyournameandtitle.Wemayalsousedirectquotes,aswewillbeusinganaudiorecorderforthisinterviewtoensurewedonotmisquoteanythingsaidtous,ormisinterpretanyinformation.

o Iftheanswerisyes,telltheintervieweethatwewillcontactthembeforewereleaseanydocumentationandaskifthecontentwrittenwithregardstotheirinterviewisanaccuratereflectionoftheirintentions/opinions.

Ø Askquestions1DataScience/ComputerScienceRelatedInterviewee

1. Haveyouhadanyprojectsthathavehadtoimplementthecreationofananalyticsdashboard/visualinterfacefromarepository?i. Ifso,pleaseelaborateontheprocessesthattheproject(s)underwent,the

challengesthatthespecificproject(s)encountered,andanythingelseimperativethatyoumightfeelisusefultous.

ii. Ifyouhadtochoosewhichstepsoftheprocesswerethemostimportant,whichstepswouldyouchooseandwhy?

2. Whattimeframesdotheseprojectsusuallytake?i. Iftheyvary,whattrendsdoyouseeinregardstothequalityoftheproject

versusthetimespentonit?3. Whatsetbacks,ifany,haveyourprojectsusuallyencountered,andaretherewaysto

preemptivelycounterthem?4. WhatexperiencehaveyouhadwithEconomics?Doyouhaveanyfamiliaritywith

economicmodeling?Ifso,pleaseexplainwhateconomicmodelingyouarefamiliarwith.

5. WhatdashboardshaveyouseenthatlookthebestforBigData(orspecificallyeconomicdata)?Pleaseexplaintheirmostimportantfeatures.

6. WhatwouldyousayaretheProsandConsofusingawebbaseddashboardversusanexecutablepackagepreinstalledonaPC?

7. Doyouknowofanyresourcesthatyouthinkwouldhelpusout?Ifso,whatarethey?

1Notallquestionswillbeasked.Dependingontheexpertise/experienceofthepersonandtime,questionsmaybeomittedduringtheinterviewastherelevanceoftheintervieweevariesandastimebecomesalimitingfactor.

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EconomyModelingrelatedInterviewee1. Howusefulwouldyouforeseeanaccessiblerepositoryofeconomicmodels?2. Whateconomicmodelswouldyouseeusefultoputintothisrepository?3. Wouldtheimplementationofsucharepositorybeusefulinteaching/research

environments?Why?4. Haveyoueverworkedwithateamtocreatearepositoryorapplicationliketheone

wearebeingaskedtodevelop?i. Ifso,canyouelaborateonthatprocess?

5. Whatarethecurrentalternativestocreatingeconomicprojections/forecastsotherthanusingsoftware?

6. Whatmetrics/algorithms/methodshaveyoufoundhelpfulforcreatingaccurateeconomicprojections/forecasts?

7. Whattypeofvisualaidsdoyoufindtheeasiesttounderstandwhenusingcomputersoftware?

8. AsComputerSciencemajors,wedonothaveastrongbackgroundineconomics.Canyoudirectustoanyresources(papers,journals,books,etc.)thatmayhelpusdevelopabasicunderstandingofeconomicmodelingandforecasting?

WrapUpQuestions(appliestoallinterviewees)1. Haveyouhadanylanguagebarrierissuesinanyprojectsyouhaveparticipated/supervised

in?i. Ifso,whatwerethetacticsusedtogetaroundanycommunicationissues?

2. Doyouhaveanyadvicethatyoufeelwouldapplytoourproject?TechnicalQuestions

1. Reiterate:canweusethisinformationasacitationinourdocumentation?Wewillcitethedateandtimeofthisinterview,aswellastheinterviewee’snameandtitle.i. Iftheanswerisyes,telltheintervieweethatwewillcontactthembeforewe

releaseanydocumentationandaskifthecontentwrittenwithregardstotheirinterviewisanaccuratereflectionoftheirintentions/opinions.

2. Wouldyouliketobenotifiedaboutourprogress?i. *Ifallowedbysponsortoshareresearch:Wearedevelopingawebsitethat

willdocumentourjourneyinMoscowwithandwithoutrelationtothisproject.Ifyouwishtobenotified,wecanaddyouremailtothelistwhenthereisupdatestothewebsite.

ii. *Ifnotallowedbysponsortoshareresearch:Wecanshareourfinishedprojectproposalattheconclusionoftheterm.Afterwards,oursponsorwishestokeepourresearchprivate.

Ø Thanktheintervieweeagainfortakingthetimetomeetwithus,andexchangecontactinformationsowecancontinuecorrespondenceifnecessaryandsendthemtheinterviewnotesifneeded.

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Appendix C: Dashboard Questionnaire

• GeneralQuestions:o Ageo Field/backgroundo Areyouaninteractive/hands-onlearner?Ordoyouprefertolearnthrough

textbooksorpapers?o Wouldyoubeinterestedinaplatformthatallowsyoutointeractand

experimentwithpre-codedeconomicmodels?o Wouldyoubeinterestedinaplatforminwhichproblemsareposted,and

usersareabletosubmittheirownsolutionsusingtheeconomicmodels?Thisplatformwillallowemployersandprofessorstoseestudent’sworkcapabilities,andwouldpotentiallyleadtoscholarshipgrantsandevenjoboffers.

• TechnicalQuestions:o Areyoucomfortableusingthecommandlinetointeractwithcode?o Wouldyoupreferacolorfulandcompletedashboardwithvisualizations?Or

asimpleinput/outputofcalculations?

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Appendix D: Model Descriptions World Trade Model TheWorldTradeModelevaluatesindicatorsforcountriesoveraspecifiedtimeperiod,andoutputsafilethatgivesdetailedanalysisoneachpossiblecountrypossible.Theworldtrademodeltakesintwodatasetsasaninput.Thedatathatisanalyzedwasgatheredfromtwomainsources,UNComtradeandWorldBankOpenData.

Thealgorithmitselfusesthedatamentionedabove,andthencomputesindicatorsforeachcountryincludedinthedata.Keepinmindthatthetwodatasetshaveadifferentamountofcountries,soweonlyusecommoncountriesbetweenthem.Eachcountryhasthefollowingindicators:Alloftheindicatorsarecompiledintoacsvfile.

Figure10:CLIInterfaceforWorldTradeModel

TheCLI(commandlineinterface)takesinadesirednumber,andthencallscertainfunctionsdependingonthequery.ThesefunctionsuseaDataFrameconstructedfromthedata,andthencalculatestherankstoreturntotheuser.

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Figure11:Calledfunctionscorrespondingtopossiblequeries

Thesefunctionswillreturnthedesiredresults,andtheCLIwilldisplaythecorrectdata,andthenremainonstandbytoreceivefurtherqueries.

Figure12:Queryfortop5countriesin2003,accordingtoTradeIndex

OnceauserisdonewiththeCLI,theycanenter0toterminatethecode,andalocalcopyoftheindexcalculationswillbesavedinthesamedirectoryasthepythoncode.This

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fileiscalledindexData(ordered),andhasthecountrieslistedtopdownaccordingtotheirrankthroughtheyearsoverall.

Table4:SnippetofOutputCSVforWorldTradeModel

Country 2000 2001 2002 2003

China 0.03506 0.038745 0.044938 0.051997

EU-28 0.109885 0.115268 0.118873 0.119441

USA 0.109784 0.106438 0.095677 0.085858

Germany 0.077324 0.083203 0.085019 0.088815

Japan 0.067429 0.058729 0.057516 0.056005

Rep. of Korea 0.024236 0.021904 0.022423 0.022997

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Cobb-Douglas Production Function Model TheCobb–Douglasmodelisaproductionfunction,widelyusedtorepresentthetechnologicalrelationshipbetweentheamountsoftwoormoreinputs,particularlyphysicalcapitalandlabor,andtheamountofoutputthatcanbeproducedbythoseinputs.Thismodelwaschosenbecauseitisthemostversatilemodeltorepresentwhatacompanywouldneedtodoinordertosignificantlyaffecttheirprofits,marketingstrategiesforcertainproducts,orotherservicestheyseektoanalyzeTheinputdataofthemodelcanbeseeninTable5below.

Table5:TheinputdatafortheCobb-DouglasProductionFunctionModel

Withthisdata,youcancalculatetheregressionmodel(Figure13below).

Figure13:Regressionresultsona3Dsurface

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Portfolio Model Theportfoliomodeltakesadatasetofportfoliosasaninputandgeneratesthe

optimalweightsforeachassetthatproducethemostprofitableportfolio(basedonhistoricalreturnoverrisk).

Table6:InputDatasetofAssets’ReturnValues

ASSET RETURN VALUE

Gazprom 0.0114 -0.0037 -0.0335 0.0164 -0.0168 …

Aeroflot -0.0311 -0.0073 -0.0368 0.0229 0.0329 …

Sberbank 0.0090 -0.0206 -0.0601 0.0075 -0.0023 …

Nornickel 0.0183 -0.0105 -0.0434 -0.0055 0.0071 …

Mechel 0.0024 -0.0007 -0.0421 0.0022 0.0017 …

Thismodelfirsttakesina.csvfile(exampleaboveinTable6)consistingofreturn

valuesoffivedifferentassetsbaseondifferentobservationtimes.Theuseristhenpromptedtochoosebetweentwocalculationmethods–MarkowitzOptimizationandTangencyPortfolioOptimization.ThecalculationsforthesetwooptimizationmethodscanbeseeninFigure14below.

Figure14:Formulasforoptimizationcalculations

Themodelthenassignseachassetarandomweightandcalculatesthemeanandrisk(whichisdeterminedbythestandarddeviation.Forallnormalrandomizedportfolio,theyhavesimilarmeanandstandarddeviationdistribution,asaresulttheyhavethesameefficientfrontier(alineofhighestreturnforeachrisk).InFigure15,theyellowlineshows

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theefficientfrontieroflargerandomportfolios;becausetheexampledatasetonlyconsistsoffiveassetsandtenobservationsforeachasset,thescatterplotonlyhaveasmallpartoverlappingtheefficientfrontier.

Figure15:Efficientfrontierofrandomlygeneratedportfolios

Thefinalresultofthemodelistogetthemostoptimalweightthatcangeneratethemostreturnandleastrisk.Theseoptimizedportfoliosarerepresentedbytheyellowpointsonthescatterplot.TheoptimizationtoolthismodelusesisConvexOptimization(CvxOpt).ThiscanbeseenbelowinFigure16.

Figure16:FormatforCvxOptPythonfunction

Finally,thisfunctioniscalledinthemainfunction(Figure17)toproducetheoptimalweights(Figure18).

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Figure17:MainfunctionofPortfolioOptimization

Figure18:Finaloutputofoptimizedweights

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Scoring Model Thescoringmodelingeneralneedsdatathatcangivethemostdetailsonacustomer.Thiscanincludedetailslikeage,creditscore,currentloans,andassets.Thisdatawillbeusedinanalgorithmthatwilldecideifthecustomerisvalidforarequestedloanthroughamachinelearningprocess.Thismachinelearningprocesswillusetrainingdatatotunethemachinecorrectly.Thiswillinturnmakeaalgorithmthatcandecidetheloanapprovalstatusbasedoffofhistoricaldata,andcurrentdata.

ThespecificmachinelearningprocesswewillbeusingiscalledRandomForest.Thisalgorithmusesmultipledecisiontreesinordertocomeupwithaverdictforadecision.

Afterthemodelistrainedfromtestdata,itreturnsthevariablesfromthedataandhowmuchofanimpacttheyhaveondeterminingwhetherornotapotentialcustomerwouldpaybacktheirloan.Thisiscalledvariableimportance,andtheresultsfromtheinitialtrainingdatasetcanbeseeninFigure19.

Figure19:Graphofvariableimportance

Thismodel,throughtheuseofauserinterface,cantakeindataofapotentialloancustomeranddeterminewitharound77.7%accuracywhetherornottheywillpayofftheirloan.Thismodelisdesignedtohelpbanksmakemoreeducateddecisionsonwhotheyshouldgivealoanto.