Inattentive Professional Forecasters

Philippe Andrade

Description

Niveau: Supérieur, Doctorat, Bac+8
Inattentive professional forecasters? Philippe Andrade Banque de France & CREM Herve Le Bihan Banque de France First draft: January 2009 This draft: November 2011 Abstract We use the ECB Survey of Professional Forecasters to characterize the dynamics of expec- tations at the micro level. We emphasize the following two new facts: forecasters (i) fail to systematically update their forecasts and (ii) disagree even when updating. We also find, as previous work on other surveys does, that forecasters have predictable forecast errors and dis- agree. We argue that these facts are altogether qualitatively supportive of recent theories in which agents are inattentive when they form their expectations. More precisely, they are in line with a model where agents imperfectly process information due to both sticky information a la Mankiw-Reis, and noisy information a la Sims. However, building and estimating such an expectation model, we find that it cannot quantitatively replicate the error and disagreement observed in the SPF data. Given how inaccurate they are, professionals agree too much to be consistent with the inattention model we test. Keywords: Expectations, imperfect information, inattention, forecast errors, disagreement, business cycle JEL classification: D84, E3, E37 ?We thank our discussants Bartosz Mackowiak, Ernesto Pasten and Gregor Smith as well as Carlos Carvalho, Olivier Coibion, Christian Hellwig, Anil Kashyap, Noburo Kiyotaki, Juan Pablo Nicolini, Giorgio Primiceri, Sergio Rebelo, Jonathan Willis, Alexander Wolman, Michael Woodford, Tao Zha and seminar participants at the Banque de France, ECB, New-York

• forecasters


• professionals


• expectation


• aggregate forecast


• cross-section dispersion


• generate disagreement


• rather than


• rational expectation


• heterogeneity among


• forecast errors

Sujets

• Nicolini
• Banque de France
• Le Bihan
• Andrade
• Woodford
• Carvalho
• Rubene (floorball club)
• Rebelo

Informations

• Publié par: profil-zyak-2012
• Nombre de lectures: 27
• Langue: English

Extrait

Inattentiveprofessionalforecasters ∗ PhilippeAndradeHerve´LeBihan BanquedeFrance&CREMBanquedeFrance Firstdraft:January2009 Thisdraft:November2011 Abstract WeusetheECBSurveyofProfessionalForecasterstocharacterizethedynamicsofexpec- tationsatthemicrolevel.Weemphasizethefollowingtwonewfacts:forecasters(i)failto systematicallyupdatetheirforecastsand(ii)disagreeevenwhenupdating.Wealsofind,as previousworkonothersurveysdoes,thatforecastershavepredictableforecasterrorsanddis- agree.Wearguethatthesefactsarealtogetherqualitativelysupportiveofrecenttheoriesin whichagentsareinattentivewhentheyformtheirexpectations.Moreprecisely,theyareinline withamodelwhereagentsimperfectlyprocessinformationduetobothstickyinformationa` laMankiw-Reis,andnoisyinformationa`laSims.However,buildingandestimatingsuchan expectationmodel,wefindthatitcannotquantitativelyreplicatetheerroranddisagreement observedintheSPFdata.Givenhowinaccuratetheyare,professionalsagreetoomuchtobe consistentwiththeinattentionmodelwetest. Keywords:Expectations,imperfectinformation,inattention,forecasterrors,disagreement, businesscycle JELclassification:D84,E3,E37 ∗ WethankourdiscussantsBartoszMac´kowiak,ErnestoPaste´nandGregorSmithaswellasCarlosCarvalho, OlivierCoibion,ChristianHellwig,AnilKashyap,NoburoKiyotaki,JuanPabloNicolini,GiorgioPrimiceri,Sergio Rebelo,JonathanWillis,AlexanderWolman,MichaelWoodford,TaoZhaandseminarparticipantsattheBanquede France,ECB,New-YorkFed,PhiladelphiaFed,San-FranciscoFed,UniversityParis1andattheconferencesESEM 2009,AFSE2010,CESIfoon“MacroeconomicsandSurveyData”andSED2010forusefulcomments.Allremaining errorsareours.WearealsogratefultoSylvieTarrieuforsuperbresearchassistanceaswellastoClaudiaMarchini andIevaRubenefortheirhelpwiththeSPFdata.ThispaperdoesnotreflectnecessarilytheviewsoftheBanque deFrance.e-mails: [e-mail] , [e-mail] 1

1Introduction Modelsinwhichimperfectinformationandtheformationofexpectationsactasatransmission mechanismofeconomicfluctuations—inthespiritofFriedman(1968),Phelps(1968)andLucas (1972)—haverecentlyregainedinterestinthemacroeconomicliterature. 1 Imperfectinformation, inparticular,hasbeenrelatedtotheinattentionofagentstonewinformation,abehaviorthatcan berationalizedbycostlyaccesstoinformationandlimitedprocessingcapacities. 2 Oneappealof thesemodelsistoprovideanalternativechanneltostickypricestoexplainthepersistenteffects oftransitoryshocks—andinparticular,monetaryshocks—ontheeconomy.Moreover,this approachcanparsimoniouslyaccountforpatternsofindividualexpectationsobservedinsurvey datathatareatoddswiththestandardperfectinformation,rationalexpectationframework,such aspredictableforecasterrorsandforecastsdifferingacrossforecasters. 3 Inthispaper,weexploitthepaneldimensionofsuchasurveyofforecasts—namelytheECBSurvey ofProfessionalForecasters(SPF)—toproducenewmicrofactscharacterizingtheformationof expectations.TheECBSPFisaquarterlypanelstartingin1999surveyingaround90forecasting unitsineitherpublicorprivateinstitutionsandallowstotracksequencesofforecastsmadebythe sameinstitution.Wethenelaborateonthesenewfactstoassesswhethermodelsofinattention accuratelydescribethebehaviorofforecasters.Wefocusontwotypesofinattentionmodels thathavebeendiscussedintherecentliterature.Ontheonehand,stickyinformationmodels developedbyMankiw&Reis(2002)andReis(2006a,b),inwhichagentsupdatetheirinformation setinfrequentlybutgetperfectinformationoncetheydo.Ontheotherhandnoisyinformation modelsproposedbyWoodford(2002),Sims(2003)andMac´kowiak&Wiederholt(2009),inwhich agentscontinuouslyupdatetheirinformationbuthaveanimperfectaccesstoitateachperiod. 1 See,amongothers,Woodford(2002),Hellwig&Veldkamp(2008),Angeletos&La’O(2009)andLorenzoni(2009). Imperfectinformationisalsocrucialintherecentwelfareanalysisofinformation.See,amongothers,Morris&Shin (2002),Angeletos&Pavan(2007)orAdamor&Weill(2009).Veldkamp(2009)andMankiw&Reis(2010)provide surveys. 2 SeeMankiw&Reis(2002),Sims(2003),Reis(2006a,b),andMac´kowiak&Wiederholt(2009). 3 SeeforexampleMankiw,Reis&Wolfers(2003). 2

Assomerelatedpreviouswork,weuseprofessionalforecastdatatotestimperfectinformationmod- els.Expertforecastersmaynotberepresentativeoflesssophisticatedagents,sinceprofessionals obviouslyallocatesubstantiallymoretime,human,collectingandcomputingresourcestothetask offorecastingmacroeconomicvariables.However,Carroll(2003)showsthattheopinionofprofes- sionalforecastersspreadstofirmsandhouseholds,andhencealsoinfluencestheirexpectationsand decisions.Furthermore,weexpectprofessionalforecasterstobetheagentsinthebestpositionto payattentiontotherelevantmacroeconomicinformation.Asaresult,theextentofattentionto newsamongprofessionalforecasterscanbeseenasanupperboundforotheragents’attentionto aggregateconditions. Ourpaperhastwomaincontributions.Thefirstoneistodocumenttwonewfactsrelatedto forecastrevisions,namelythat(i)forecastersdonotsystematicallyupdatetheirforecastseven whennewinformationisreleased,andthat(ii)forecasterswhoupdatealsodisagreeontheir forecasts.Theoriginalityofourapproachistoexploitthesequencesofindividualforecastsfora givenevent(sayinflationattheendofagivenyear)providedbytheECBSPFtoconstructadirect micro-dataestimateofthefrequencyofupdatingaforecast,which,toourknowledge,hasnotbeen documentedinsurveydatabefore.Theresultsshowthat,onaverage,eachquarteronly75%of professionalforecastersupdatetheir1-yearor2-yearforecasts.Thisfirstresultisinlinewiththe predictionsofasticky-informationmodel.Furthermore,inthissetupthefrequencyofupdating hasastructuralinterpretationandcorrespondstothekeyparameteroftheattentiondegree.In additiontounfrequentupdating,wealsouncoverthatforecasterswhoupdatetheirinformation setsdisagreeabouttheirforecasts.Consequently,disagreementamongexpertsisnotonlyrelated todifferencesintheinformationsetsofforecasterswhoupdatedandofthosewhodidnot,butalso tothefactthat,whentheyupdate,theyusedifferentinformationsets.Thissecondresultisin linewiththepredictionsofanoisy-informationmodel.Wealsofind,aspreviousworkrelyingon surveydatadoes,thatforecastsofexpertsexhibitpredictableerrorsandthatforecastersdisagree astheyreportdifferentpredictionsforthesamevariableatthesamehorizon.Theselattertwo 3

characteristicsareinlinewithbothstickyandnoisyinformationmodels. Thesecondmaincontributionofthispaperistoperformaformalempiricalassessmentofinat- tentionmodels.Thefactsweunderlinequalitativelysupportamodelfeaturingatthesametime twotypesofinattention;namely,sticky-informationa`laMankiw-Reis,andnoisy-informationa`la Sims.WedevelopsuchanexpectationmodelandthenusetheSPFdatatocarryoutaMinimum DistanceEstimation(MDE).Moreprecisely,theestimationprincipleistomatchthemoments characterizingtheforecasterrorsandthedisagreementgeneratedbythistheoreticalmodelwith theirempiricalcounterpartobservedintheECBSPFdata.Wefindthatthisinattentionmodel failstoquantitativelyreproducetheobservedpersistenceoftheaverageforecastingerrorstogether withtherelativelysmalldisagreementbetweenforecasters.Fittingthesmoothnessobservedinthe averageSPFforecastswouldrequireamuchlowerattentiondegreethanourmicrodataestimates. SuchalowattentionwouldinturnleadtomuchmoredisagreementthanobservedintheSPF data.Therefore,elementsothersthanthetypeofinattentionincludedinourexpectationmodel areneededtoreconciletherelativelylowdisagreementamongprofessionalsandtherelativelyhigh persistenceoftheaggregateforecastingerror. Ourpaperrelatestothevastliterature,mostlyrelyingonUSdata,thatstudiesthebehaviorof surveyforecastsandcomparesitwiththeimplicationsoftheoreticalexpectationmodels.Numerous studies(seePesaran&Weale,2006forarecentsurvey)foundsystematicaggregateforecasterrors anddisagreementinthesedata,atoddswiththeperfectinformationrationalexpectationframe- work.Here,weprovideevidenceofdisagreementandpredictableforecasterrorsforEuropeanSPF dataandforarecentsampleperiod.Wealsocomplementtheseresultsbyprovidingnewevidence ontheinfrequencyofindividualexpectationsrevision. OurworkisalsorelatedtoMankiwetal.(2003),Branch(2007),Coibion&Gorodnichenko(2008) andPatton&Timmermann(2009),whorelyonthecharacteristicsofsurveyexpectationsto assessinattentionand,moregenerally,imperfectinformationtheories.Mankiwetal.(2003)and Branch(2007)focusonthecross-sectiondistributionofforecaststocalibratethesticky-information 4

attentionparametermentionedabove.Bycomparison,weunderlinetheimportanceofinvestigating theconsistencyofthisparametervalueswithboththecross-sectiondispersionofforecastsandthe aggregateforecasterrors.Furthermore,weimproveontheirapproachbyconsideringamodel thatcanexplainthedisagreementamongforecasterswhoupdatetheirinformationset.Lastly, ratherthancalibrateit,weestimatetheattentionparameterusingalternativelydirectmicro- dataestimatesandaMDEprocedure.Coibion&Gorodnichenko(2008)lookattheconditional responsetovariousstructuralshocksoftheaggregateerroranddisagreementimpliedbysurveys todisentanglethesticky-informationandthenoisy-informationmodelsofinattention.Theyfind mixedsupportinfavorofthetwo,justaswedo.Thedistinctivefeatureofouranalysisisthatwe estimateamodelfeaturingsimultaneouslythetwotypesofinattention.Patton&Timmermann (2009)relyontheevolutionofforecastsoverdifferentforecasthorizonstostressthatdifferencesin theinterpretationofinformation—ratherthandifferentinformationsets—arethemainculprit forforecasters’disagreement.Theyleavethesourceofthesedifferentinterpretationsunexplained. Themodelweconsiderisanalternativeapproachtogeneratedisagreementthatdoesnotrelyon “deep”heterogeneityamongforecasters. Ourpaperismoreovercloselylinkedtoseveralrecentcontributionsthatrelyonaggregatetimeseries toestimatetheattentiondegreeinasticky-informationmodelofinflationdynamics(Kiley2007, Do¨pkeetal.2008,Coibion2010).Whensignificant,theresultsimplyafrequencyofupdatinga forecastrangingfrom10%to30%,wellbelowthefigureof75%weobtain.Partofthediscrepancy couldbeexplainedbythefactthatwerelyonapanelofprofessionals,sincetheseagentsmay updatemorefrequentlytheirforecaststhanothertypes.However,manyoftheserecentstudies (forinstanceDo¨pkeetal.2008,Coibion,2010)alsouseprofessionalforecasters’expectationsto performtheirestimation.Thediscrepancyisthusalsorelatedtothemethodology.Studiesthat usemacroeconomictimeseriestypicallyrelyonauxiliaryassumptionsabouttheeconomy,andare potentiallysubjecttoaggregationbiases.Bycontrast,weprovideadirect,arguablymorereliable, micro-dataestimateofthisparameterthatiskeyinstickyinformationmodels.Althoughwefind 5