IMF-季度GDP不可用國家的面板實況轉(zhuǎn)播（英）-2023.8

PanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableOmerFarukAkbalSeungMoChoiFutoshiNaritaandJiaxiongaoPIMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseoftheauthor(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.2023AUG?2023InternationalMonetaryFundWP/23/158IMFWorkingPaperResearchDepartmentPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailablePreparedbyOmerFarukAkbal,SeungMoChoi,FutoshiNarita,andJiaxiongYaoAuthorizedfordistributionbyChrisPapageorgiouAugust2023IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseoftheauthor(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.ABSTRACT:QuarterlyGDPstatisticsfacilitatetimelyeconomicassessment,buttheavailabilityofsuchdataarelimitedformorethan60developingeconomies,includingabout20countriesinsub-SaharanAfricaaswellasmorethantwo-thirdsoffragileandconflict-affectedstates.Toaddressthislimiteddataavailablity,thispaperproposesapanelapproachthatutilizesastatisticalrelationshipestimatedfromcountrieswheredataareavailable,toestimatequarterlyGDPstatisticsforcountriesthatdonotpublishsuchstatisticsbyleveragingtheindicatorsreadilyavailableformanycountries.Thisframeworkdemonstratespotential,especiallywhenappliedforsimilarcountrygroups,andcouldprovidevaluablereal-timeinsightsintoeconomicconditionssupportedbyempiricalevidence.RECOMMENDEDCITATION:Akbal,OmerFaruk,SeungMoChoi,FutoshiNarita,andJiaxiongYao.2023.“PanelnowcastingforcountrieswhosequarterlyGDPsareunavailable.”IMFWorkingPaper,23/158.JELClassificationNumbers:E37,O11,O55Keywords:Developingeconomies;fragileandconflict-affectedstates;GDP;low-incomecountries;nowcasting;sub-SaharanAfricaAuthor’sE-MailAddress:OAkbal@;SChoi@;Fnarita@;JYao@WORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableredbyOmerFarukAkbalSeungMoChoiFutoshiNaritaandJiaxiongYao11Acknowledgements.WethankHanyAbdel-Latif,MarijnA.Bolhuis,LucEyraud,JesusFernandez-Villaverde,MicheleFornino,MachikoNarita,PapaMBagnickN'Diaye,ChrisPapageorgiou,M.HashemPesaran,AndrewJohnTiffin,aswellasotherIMFcolleagues,especiallythemembersoftheAFRNowcastteam,aswellascountryteamsintheIMF’sAfricanDepartment.Thispaperispartofresearchprojectson“macroeconomicpolicyinlow-incomecountries”supportedbytheU.K.’sForeign,CommonwealthandDevelopmentOffice(FCDO)and“macroeconomicresearchonclimatechangeandemergingrisksinAsia”supportedbytheGovernmentofKorea.TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyrepresenttheviewsoftheIMFitsExecutiveBoard,IMFmanagement,orthesupportingpartners.Weareresponsibleforanyremainingerrors.Inthispaper,theterms“country”and“economy”areusedinterchangeably,whichdonotinallcasesrefertoaterritorialentitythatisastateasunderstoodbyinternationallawandpractice.IMFWORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableINTERNATIONALMONETARYFUND2Glossary 31.Introduction 42.PanelNowcastingofQuarterlyGDPGrowth 5Figure1.Ensemblenowcastsdependingondataavailability 63.EvaluationofPanelNowcastPerformance 7Figure2.Twodirectionsofout-of-sampleevaluation 8Table1.Performanceofpanelnowcasts:LGB,globalsample,average 9Table2.Performanceofpanelnowcasts:LGB,SSAsubsample,average 10Table3.Performanceofpanelnowcastsforcountrieswithoutquarterlygrowthdata:LGB 114.QuarterlyGDPNowcastsforSSACountries 12Figure3.Quarterlygrowthnowcastsandactualgrowth:UgandaandSierraLeone 12Figure4.Annualgrowthnowcastsandactualgrowth:UgandaandSierraLeone 13Figure5.DecompositionofgrowthnowcastsforUgandaandSierraLeone,2022Q3 145.Conclusion 14AnnexI.Detailsofdatacollectionandestimation 16I.A.Datacollection 16I.B.Estimation 17I.C.Howtoconstructannualnowcastsfromquarterlynowcasts 17I.D.Handlingmissingobservations—alternativesanddiscussions 18I.E.Estimatedcontributionsofinputvariables 19AnnexTable1.Countrycoverage 21AnnexTable2.Fullvariablelistwithdatasources 22AnnexII.Additionalresults 25AnnexTable3.Performanceofpanelnowcasts:LGB,EMDEsubsample,average 25AnnexTable4.Performanceofpanelnowcasts:LGB,commodityexportersubsample,average 26AnnexTable5.Performanceofpanelnowcasts:LGB,fuelexportersubsample,average 27AnnexTable6.Performanceofpanelnowcasts:LGB,tourism-orientedcountrysubsample,average AnnexTable7.Performanceofpanelnowcasts:OLS,globalsample,average 29AnnexTable8.Performanceofpanelnowcasts:OLS,SSAsubsample,average 30AnnexTable9.Performanceofpanelnowcastsforcountrieswithoutquarterlygrowthdata:OLS 31References 32INTERNATIONALMONETARYFUND3CPIConsumptionpriceindexEMDEEmergingmarketanddevelopingeconomiesFCSFragileandconflict-affectedstatesGDPGrossdomesticproductLGBLightgradientboostingregressionLIDCLow-incomedevelopingcountryNO2NitrogendioxideOECDOrganisationforEconomicCo-operationandDevelopmentOLSOrdinaryleastsquaresPPPPurchasingpowerparityRMSERootmeansquareerrorSSASub-SaharanAfricaIMFWORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableINTERNATIONALMONETARYFUND4AnotablehurdleinmonitoringeconomicactivitiesofdevelopingeconomiesisthelimitedavailabilityofquarterlyGDPstatistics.ArecentstocktakingofnationalaccountstatisticsbySilungwe,Bear,andGuerreiro(2022)indicatesthatmorethan60countriesdonotpublishofficialquarterlyGDPstatistics.Thoseincludeabout20countriesinsub-SaharanAfrica(SSA),morethanhalfoflow-incomedevelopingcountries(LIDCs),andmorethantwo-thirdsoffragileandconflict-affectedstates(FCS).1EvenincaseswherequarterlyGDPdataareavailable,thereisoftenadelayinitsrelease.Infact,forabout20countries,thistimelagexceedsonequarter.ThislimitedavailabilityoftimelyquarterlyGDPstatisticsposesasignificantchallengeforpolicymakers,particularlywhenrespondingswiftlytosuddenshiftsineconomicconditionsasinthecaseofrecentmultipleglobalshocks.Toaddressthischallenge,thispaperproposesapanelapproachtonowcastquarterlyGDPs,particularlytargetingcountriesthatdonotpublishquarterlyGDPstatistics.2Thispanelapproachassumesacommonstatisticalstructureacrosscountries,acknowledgingthatpoolingtogethercountrieswithdifferentcharacteristicsmayintroducespecificationerrors.However,itleveragesinsightsfromcountrieswithavailabledataintoeconomicactivityofcountrieswithlimiteddataavailability.Inparticular,theapproachfeaturestheuseofnontraditionaldatasourceswhicharerelativelymoreavailableacrossmanycountries.Givenapaneldatasetofcountries,anestimationmethod(e.g.,ordinaryleastsquares,OLS)cangenerateanowcastasafittedvalueofquarterlyGDP,usinginputvariablesforagivencountry.Tomitigateanobstaclethattherearestillmissingvaluesforinputvariablesinsomecountries,particularlyformorerecentperiods,thispaperproposestouseanensembleofnowcasts.Theproposedapproachcomplementsexistingeffortstoenhancetimelyeconomicassessmentsforcountrieswithdatagaps,suchasaworkstreamintheIMF’sAfricanDepartment(Barhoumiandothers2022).Constructingahigh-frequencyindicatorofeconomicactivityhasbeenalsoanareathattheIMF’sStatisticsDepartmenthasbeenfocusingon,includinginitscapacitydevelopmentagenda.Thisstudyisalsorelatedtothegrowingliteratureontheuseofnontraditionaldatasourcesforeconomicanalysis,includingremotesensingdatasuchasnighttimelights(e.g.,Debbich2019;HuandYao2022;Beyer,Hu,andYao2022),Googletrends(NaritaandYin2018),andGooglePlacesAPI(Austinandothers,2021).3Therestofthepaperisorganizedasfollows.Section2providesanoverviewoftheproposedpanelnowcastingframework,whiledetailsarepresentedinAnnexI.Section3evaluatestheperformanceofpanelnowcasts.Section4demonstratesresultsforselectedcountriesinSSA.Section5concludeswithkeycaveatsandroomforimprovement.1ThisisbasedontheFCSclassificationbytheWorldBankasofMarch2023,followingtheIMF’sFCSStrategy(IMF2022a).Nowcastingisanestimationofpresentorrecentpastvaluesofunobservedvariablesusingobservedhighfrequencyindicators.Itispartofamoregeneralapproachtoproduceacompositeindicatorthatcorrelateswiththelevelofeconomicactivities,bycombiningdataavailableatahighfrequency.3GoogleTrendsdatahavebeenwidelyusedfornowcastingandforecastingpurposes.See,forexample,paperscitedinNaritaandYin(2018).Morerecently,Woloszko(2020)setsupaGDPgrowthnowcastframeworknamedOECDWeeklyTrackerfor46OECDandG20countriesusingGoogleTrendssearchdatawithaneuralnetworkmodel(ensembledmulti-layerperceptronregressions).Cevik(2022)usestravel-relatedonlinesearchqueriestoforecasttouristarrivalsfromtheU.StoTheBahamas.INTERNATIONALMONETARYFUND5wcastingofQuarterlyGDPGrowthTheproposedframeworkassumesacommonstatisticalrelationshipacrossalleconomiesinthesample.LetyitdenotequarterlyGDPgrowth,calculatedbylogdifference,fromthesamequarterayearago(i.e.,year-on-yeargrowth)foreconomyiandtimet.Similarly,allotherinputvariables,denotedbyXit,aretransformedintoquarterlygrowthfromthesamequarterayearago.Conceptually,ourobjectiveistoconstructanowcastofyitbyestimatingconditionalexpectationE[yit|Xit],whichisanarbitraryfunctionofXit,whichmayingeneraldependsoncountryiandperiodt.Ourpanelapproachreliesonastrongassumptionthatthisfunctioniscommonacrosscountriesandperiods,denotedbyf(Xit),withoutindexiorperiodt,suchthat:whereeitisanowcasterroranditisanowcastofquarterlyrealGDPgrowthfromthesamequarterayearago4.SinceyitisnotobservableforthecountriesthatdonotpublishquarterlyGDPdata,thefunctionalrelation,f(.),isestimatedusingthepanel-countrygroupwithavailabledata.Toestimatefunctionf(Xit),weuseamachinelearningtechniquecalled“l(fā)ightgradientboostingregression”(LGB5)aswellasOLS.FortheLGB,weusethedefaulthyperparameters.SeeAnnexI.BfordetailsandseeAnnexI.Cforhowweconstructanowcastofannualgrowthbasedonanowcastofquarterlygrowth.AkeychallengeofpanelestimationistocollectasetofinputdataseriesXitthatarecommonlyavailableforthesetofcountriesinthesample.Wecollect117quarterlyindicatorsthatarerelatedtoeconomicactivity,amongwhich76indicatorsarefromnontraditionaldatasources,forasmanyas200economiessince2008Q1(seeAnnexI.Afordetails)6.Evenafterleveragingnontraditionaldatasources,itisstillchallengingtocoverallcountry-periodpairsofinterest,especiallyforrecentperiods.Inourdataset,thevariablesthatareavailableforalleconomiesforallsampleperiodsaresixglobalcommodityprices,twoglobalfinancialindexes(U.S.2-yearbondyields,U.S.stockmarketvolatilityindex),and14world-widesearchvolumeindexes(outof28inoursample).Strictlyspeaking,wecanusethese22variablesonly(togetherwithquarterandcountrygroupdummies),butthesemaybetoofewtocapturegrowthdynamics.Toreflecttheinformationfromallavailableinputvariablesasmuchaspossible,weconsideranensembleofnowcastsbytakingtheaverageoverallspecificationsbasedondataavailableforeachcountryineachperiod.Thisaverageensembleensuresthatnowcastsaregeneratedforallcountriesandperiods,atleastusingtheminimumsetofthe22inputvariablesasmentionedabove.Then,ifmorevariablesareobservedforacountryinaperiod,thenowcastreflectsmoreinformation.Forcomparison,wealsoconsiderthemost‘general’specification(i.e.,thespecificationwhosenumberofinputvariablesisthelargest,dependingonacountry-periodpair)andtheleastone(i.e.,thespecificationwhosenumberofinputvariablesisthesmallest—amodelwiththe22variablesmentionedabove).Correspondingly,threeensemblenowcastsaregenerated,named“average,”“maximum,”and“minimum”models,withthe“average”ensemblebeingthebaseline(Figure1).SeeAnnexI.Dformorediscussionsinhandlingmissingobservations.ManycountriesimplementchainlinkvolumemeasureswherebytheheadlineGDPgrowthratemightdifferfromtheonederivedfromtheaggregationofcomponents.ThispaperdoesnotimposeanystructuralaggregationfromGDPcomponentstoheadlineGDPgrowth,insteadaimstoutilizeinformationgatheredfromgranulardataintoasingleheadlinegrowth.LightgradientboostingisadecisiontreebasedlearningalgorithmItsefficiencylowmemoryuse,andcapacityofhandlinglarge-scaledatamakeitanadvantageousmachine-learningalgorithmforthistoolkit.6AfutureworkconsideredforthisstudyistoextendthedatacoveragewithIMFHighFrequencyDataHub.INTERNATIONALMONETARYFUND6Wealsoconsiderdifferentestimationsubsamplestoleveragesimilaritywithincountrygroups.Tomitigateacaveatofassumingacommonstatisticalstructureacrosscountriesofdifferentnatures,nowcastsarealsoestimatedbysubsamples,basedonregions(e.g.,SSA)andexporttypes(e.g.,fuelexporters).Subsamplenowcastsaregeneratedforthoseeconomieswithinthesubsample.Tomaximizethecoverage,ourbaselineremainstousethefullsample(labeled“Global”),althoughsubsamplingeconomieswithsimilarnaturesmayimprovenowcastperformance.Figure1.EnsemblenowcastsdependingondataavailabilitySource.Authors.NotesThisisaconceptualdiagramtoexplainhowensemblenowcastsareconstructed,basedondataavailability.Cellsingreenindicatethecountry-periodpairswheredataareavailable,andcellsareinredotherwise.ForcountryAfor2022Q2and2022Q3,allinputdataX1,X2,andX3areavailable,andtherefore,allmodelsF,G,andHcanbeusedtogeneratenowcasts.Insuchacase,the“average”modelisconstructedbytakingtheaverageofthreenowcastsfromthethreemodels.The“maximum”modelinthiscaseismodelF,whichhasthemaximumnumberofinputvariablesineachperiodforcountrypairs,i.e.,notmaximumnumberofvariablescoveringallperiodsforasinglecountry.ForcountryBfor2022Q2,modelsGandHcanbeused,andthe“average”modelistheaverageoftwonowcastsfrommodelsGandH.The“maximum”modelinthiscaseismodelG.ModelHonlyusesinputvariableX1andcanbeusedallcasessothatthe“minimum”modelismodelHforallcountry-periodpairs.ForcountryBfor2022Q3andcountryCfor2022Q2and2022Q3,onlymodelHcanbeusedsothatthe“minimum,”“average,”and“maximum”modelsareallthesame,i.e.,modelH.Toprovidemoreinsights,contributionsofinputvariablestoanowcastarealsoestimated.InthecaseofLGB,weuseanapproachnamed“SHAP”(SHapleyAdditiveexPlanations)byLundbergandLee(2017).TheSHAPvalues{kit}ofinputvariables{Xkit}arelocallyadditivesothatthesumofthecontributionsfromallinputs(aswellastheconstantterm)isequaltothenowcastitforeachpairofcountryiandperiodtasfollows7:it=(yit)+xkit,kwhere(yit)denotesthesamplemean.InthecaseofOLS,theSHAPvalueissimplycoefficientF?ktimesthevalueoftheinputvariableXkit(i.e.,kit=F?kXkit).Tounderstandwhathadcontributedtogrowthinthequarterofinterestsinceaquarterbefore,wealsocalculatethedifferencesinSHAPvaluesovertime.ThisapproachnecessarilyincludestheresidualintheNotethattheSHAPcontributionsaredescribedascausaleffectsfromthefactorstooverallgrowthsincethereisalsothereversecausality.Section4providesamoredetaileddiscussion.IMFWORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableINTERNATIONALMONETARYFUND7previousquarter(i.e.,i,t?1些yi,t?1?i,t?1)asoneofthefactorscontributingtonowcastitinthequarterofinterestasfollows:it?yi,t?1=yit?i,t?1+i,t?1?yi,t?1=x(kit?k,i,t?1)+(?i,t?1),kassumingthatthereisnochangeinthesetofinputvariables{xkit}betweenperiodt?1andt.TheSHAPvaluesforensemblenowcastsneedanotherresidualtermduetochangesinspecificationsovertime(seeAnnexI.Efordetails).ationofPanelNowcastPerformanceIntheory,panelnowcastsmayleadtolowerin-samplefitthancountry-specificnowcastsbutmaypotentiallyleadtobetterout-of-samplefitbysufferinglessfromoverfittingissues.Keepingthesamecomplexityofnowcastmodels,in-samplefitmaybebetterifthemodelisestimatedspecifictoacountryofinterestthanifitisestimatedinapanelsetup.Butout-of-samplefitofpanelnowcastsmaybepotentiallygoodbecausethestrongassumptionofacommonstructureovermanydifferentcountriespreventsthenowcastsfrombeingoverfittedtoobserveddatapoints.Theoverfittingissueisakeyconcernparticularlywhenusingamachinelearningtechniquewithahighmodelcomplexity(see,e.g.,Barhoumiandothers2022,FigureA1).Out-of-samplefit,wherequarterlygrowthdataareavailable,isevaluatedintwodirections:country-wiseandperiod-wise(Figure2).Country-wiseevaluationisbasedonanestimationsampleexcludingacountrywhoseobservationswillbeusedonlytocalculaterootmeansquarederrors(RMSEs).Thispseudoout-of-sampleperformanceindirectlyassesseshownowcastscouldperformforcountrieswithoutquarterlygrowthdata.Italsoevaluateshowdatainothercountriescanhelpnowcastgrowthinanothercountryofinterest.Ontheotherhand,period-wiseevaluationisbasedonanestimationsamplewithoutrecentperiods(e.g.,upto2021Q3.Thisdirectionofpseudoout-of-sampleperformanceassesseshowhistoricalstatisticalrelationshipscanhelpestimatenowcasts.Althoughperiod-wiseout-of-sampleassessmentsaremorecommon,country-wiseassessmentsfitbettertoourpurposetoprovideinsightstocountrieswithlimiteddataavailability.Neitherofthesecountry-wiseandperiod-wiseevaluations,however,canbeusedtodirectlyevaluatetheperformanceofpanelnowcastsforcountrieswithoutquarterlygrowthdatabecausenowcasterrorscannotbecalculatedwithoutobservedquarterlygrowthdata.IMFWORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableINTERNATIONALMONETARYFUND8Figure2.Twodirectionsofout-of-sampleevaluationPanelA:Country-wise20192019Q42020Q12020Q22020Q32020Q42021Q1CountryA:GDPCountryA:X1CountryA:X2CountryA:X3CountryB:GDPCountryB:X1CountryB:X2CountryB:X3CountryC:GDPCountryC:X1CountryC:X2CountryC:X3RemovedRemovedRemovedRemovedRemovedOnlyannualGDPisavailableforcountryC.PanelB:Period-wise20192019Q42020Q12020Q22020Q32020Q42021Q1RemovedRemovedRemovedOnlyannualGDPisavailableforcountryC.CountryA:GDPCountryA:X1CountryA:X2CountryA:X3CountryB:GDPCountryB:X1CountryB:X2CountryB:X3CountryC:GDPCountryC:X1CountryC:X2CountryC:X3RemovedRemovedRemovedSource.Authors.NotesThisisaconceptualdiagramtoexplainhowoutof-sampleperformanceofpanelnowcastsisevaluated,wherequarterlyrealGDPgrowthdataareavailable.“Removed”indicatesdatapointsthatareexcludedfromnowcastestimationandthenusedonlytocalculateoutof-sampleRMSEs.Inthisexample,country-wiseout-of-sampleRMSEsareevaluatedforcountryBfrom2020Q1to2021Q1.Period-wiseout-of-sampleRMSEsareevaluatedforcountriesAandBfrom2020Q3to2021Q1.QuarterlygrowthdataarenotavailableforcountryC,forwhichthesetwoevaluationscannotbeconducteddirectly.Resultsshowthatout-of-samplefitisgenerallywellwhenevaluatedcountry-wisealthoughitispoorperiod-wise.Evaluatedfor15selectedcountriesinSSA,country-wiseout-of-samplefitofourbaselinepanelnowcast(whichisthe“average”ensemblenowcastbasedontheLGBestimatedusingthefull“global”sample)isrelativelywellcomparedtoana?verandomwalknowcast(whichisjustaone-quarterlagofquarterlyrealGDPgrowth).TheTheilUindex8—theratioofRMSEsovertheRMSEsoftherandomwalknowcast—islessthan75percentfor9outof15countries(Table1,PanelA).Itislessthan50percentforfivecountries,implyingareductionofnowcasterrorsbymorethanahalf.TheTheilUindexisveryoffexceeding500percentforTanzania,butitisbecausetherandomwalknowcastratherperformsverywellinthiscase,andthepanelnowcast’sout-of-sampleRMSEsis5.27percent,whichisweakbutnotsomuchasimpliedbytheverylargeTheilUindex.Incontrast,period-wiseout-of-sampleRMSEsindicatepoorperformance,showinglargerRMSEsthantherandomwalknowcast(Table1,PanelB).Thispoorperformancepartlystemsfromthedifficultytocapturetheveryvolatilegrowthpathsincetheonsetofthepandemicbutalsosuggeststhatitmaynotbeeasytolearnaboutthepresentfromthepast.Still,relativelygoodcountry-wiseperformanceimpliesthatpanelnowcastsmaybehelpfultolearnfromdataonothercountriestoprovideinsightsintoeconomicsituationsincountrieswheredataavailabilityislimited.Evaluationofout-of-samplefitforsubsamplesindicatesthepotentialtoimprovethefitbytailoringestimationsamplestocountrycharacteristics.IftheLGBmodelisestimatedonlyfor25SSAcountriesforwhichquarterlygrowthdataareavailableinoursample,thenperiod-wiseout-of-sampleperformancetoshowtheTheilUindexoflessthanone,andcountry-wiseperformancealsotendtoimprovetosomeextent(Table2).Suchimprovementsarenotsoobviousforothersubsampleestimations(AnnexTables3,4,5,6),butitmaybeworthexploringawaytoidentifyasubsamplethatleadstobetterout-of-samplefit,assuggestedbyBolhuisandRayner(2020).Thereisstilllargeroomforimprovement.EvenwhentheTheilUindexissmall,ourpanelnowcaststendtoproducelargerout-of-sampleRMSEsthanthoseofcountry-specificnowcasts(e.g.,Barhoumiandothers2022,showingabout2percentofRMSEsfortheselectedbestmodels).Out-of-samplebiasisalsolarge.Ourpanelnowcaststendtobecorrectintheirdirections(i.e.,theyindicateapositiverateofgrowthwhenactualgrowthis8TheTheilUindexversionusedinthispaperisalsoreferredasTheilU2index.IMFWORKINGPAPERSPanelNowcastingforCountriesWhoseQuarterlyGDPsareUnavailableINTERNATIONALMONETARYFUND9positive,andviceversa)withprobabilityofmorethan70percentfor9outof15cases,butinsomecases,thedirectionalpredictionsarecorrectjustwithprobability?(Ghana,Mauritius).LGB-basednowcaststendtoperformbetterthanOLS-basednowcasts(AnnexTables7,8),implyingthatthereisroomtoexploreabetterestimationmethodandabettercalibrationofhyperparameters.Table1.Performanceofpanelnowcasts:LGB,globalsample,averagePanelA.Country-wiseevaluationCountry2019Q1-2022Q3esizemplemplebiasontsampleOut-of-samplebiasmpleectionmpleectionestmpleOut-of-sampledirectionmplemplestmpleOut-of-sampleTheilUAngola(15)Botswana(15)CaboVerde(15)Cameroon(15)CtedIvoire5)Ghana(15)Kenya(15)Lesotho(15)iusNamibia5)Nigeria)Seychelles15)SouthAfrica(15)Tanzania15).97.13Uganda(15)PanelB.Period-wiseevaluationTestsampleperiod(samplesize)mplemplebiasontsampleOut-of-samplebiasmpleectionmpleectionestmpleOut-of-sampledirectionmplemplestmpleOut-of-sampleTheilUQ2022Q3Q2022Q3Q2022Q3.07Sources:Authorsestimations(seeAnnexTable2fordatasources).NotesTheestimationsampleincludes7observationsfor127economiesfrom