欧洲央行-货币政策与风险增长：制度质量的作用 Monetary policy and growth-at-risk the role of institutional quality

EUROPEANCENTRALBANK

EUROSYSTEM

LorenzEmter,AfonsoS.Moura,RalphSetzer,NicoZorell

WorkingPaperSeries

Monetarypolicyandgrowth-at-risk:theroleofinstitutionalquality

No2989

Disclaimer:ThispapershouldnotbereportedasrepresentingtheviewsoftheEuropeanCentralBank(ECB).TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseoftheECB.

ECBWorkingPaperSeriesNo29891

Abstract

Thispaperanalyseshowcountry-specificinstitutionalqualityshapestheimpactofmon-

etarypolicyondownsideriskstoGDPgrowthintheeuroarea.Usingidentifiedhigh-frequencyshocksinagrowth-at-riskframework,weshowthatmonetarypolicyhasahigherimpactondownsiderisksintheshorttermthaninthemediumterm.However,thisresultfortheeuroareaaveragehidessignificantheterogeneityacrosscountries.Ineconomieswithweakinstitutionalquality,medium-termgrowthrisksincreasesubstan-tiallyfollowingcontractionarymonetarypolicyshocks.Incontrast,theserisksremainrelativelystableincountrieswithhighinstitutionalquality.Thissuggeststhatimprove-mentsininstitutionalqualitycouldsignificantlyenhanceeuroareacountries’economicresilienceandsupportthesmoothtransmissionofmonetarypolicy.

Keywords:Euroarea,growth-at-risk,institutionalquality,monetarypolicytransmissionJELClassification:C23,E52,F45,G28,O43

ECBWorkingPaperSeriesNo29892

Non-technicalsummary

25yearsaftertheintroductionoftheeuro,theeuroareacountriesarestillheterogeneousintermsofeconomicstructures.Thisisparticularlyevidentinstandardindicatorsofinstitu-tionalquality,suchastheWorldBank’sWorldGovernanceIndicators.Whilesomeeuroareacountriesareclosetotheglobalfrontier,othersarelagging.

Itiswidelyrecognisedthatcross-countrydifferencesininstitutionsandothereconomicstructureshaveimportantimplicationsforthetransmissionoftheECB’smonetarypolicy.Inparticular,structuralheterogeneitycancontributetocross-countrydifferencesintheresponsesofoutputandinflationtomonetarypolicychanges.This,inturn,maycontributetorealornominaldivergences,makingitlesslikelythatthecommonmonetarypolicyisalignedwitheconomicconditionsineachindividualeuroareacountry.

Inthispaper,weexploreifdifferencesininstitutionalqualityacrosseuroareacountriesalsomatterfortailrisksintheaftermathofmonetarypolicyshocks.Whenpolicymakersconsidertheimpactofmonetarypolicychangesonfutureeconomicactivity,theytypicallyfocusonthemostlikelyscenario,i.e.themeanofthe(conditional)distributionoffutureGDPgrowth.However,centralbanksalsoincreasinglyanalysetherisksaroundthecentralprojectioninquantitativeterms.Againstthisbackdrop,ourpaperaimstoshedlightontheroleofinstitutionalfactorsinshapingdownsideriskstoGDPgrowthintheaftermathofmonetarypolicyshocksinaheterogeneousmonetaryunion.

Weusethegrowth-at-riskframeworkproposedby

Adrian,BoyarchenkoandGiannone

(2019)toestimatedownsideriskstofutureGDPgrowthwithpanelquantileregressions

.Inlinewiththeliterature,wedefinegrowth-at-riskasthelowestdecileofthedistributionofpredictedGDPgrowth.Toestimatetheimpactofmonetarypolicyshocksongrowth-at-risk,wefollowthemethodproposedby

Loria,MatthesandZhang

(2024)

.Wesplitoursampleintoeuroareacountrieswithhigherandlowerinstitutionalquality,respectively,asmeasuredbytheWorldGovernanceIndicators.

WefindthatmonetarypolicyhasahigherimpactondownsideriskstoGDPgrowthintheshorttermthaninthemediumterm.However,thishidessignificantheterogeneityacrosscountries.Ineconomieswithweakinstitutionalquality,medium-termgrowthrisksincreasesubstantiallyfollowingcontractionarymonetarypolicyshocks.Incontrast,theserisksremainrelativelystableincountrieswithhighinstitutionalquality.Interestingly,expansionarymon-etarypolicyshockshaveamilderandmoresymmetricimpactthancontractionaryshocks,bothacrosscountriesandquantilesoftheconditionalgrowthdistribution.Wheninspectingthetransmissionchannels,wefindthatmedium-termrisksincreasethroughtheimpactofmonetarypolicyshocksonmacro-financialvulnerabilities,inparticularincountrieswithlowinstitutionalquality.

Theseresultshaveimportantpolicyimplications.First,ourempiricalfindingssuggestthatimprovinginstitutionalqualitycanstrengthentheeconomicresilienceofeuroareacoun-tries.Insofar,wecomplementexistingstudiesthatemphasisetheroleofbankcapitalisation,macroprudentialmeasuresormonetarypolicyinstrumentsinsteeringgrowth-at-risk.Sec-ond,ourfindingsindicatethatupwardinstitutionalconvergencewouldsupportthesmooth

ECBWorkingPaperSeriesNo29893

transmissionofmonetarypolicyintheeuroareabyensuringalesspronouncedandmorehomogeneousresponseofmedium-termgrowth-at-risktomonetarypolicytightening.

ECBWorkingPaperSeriesNo29894

1Introduction

25yearsaftertheintroductionoftheeuro,theeuroareacountriesarestillheterogeneousintermsofeconomicstructures.Thisisparticularlyevidentinstandardindicatorsofinsti-tutionalquality,suchastheWorldBank’sWorldGovernanceIndicators(WGI).Whilesomeeuroareacountriesareclosetotheglobalfrontier,othersarelagging.

Itiswidelyrecognisedthatcross-countrydifferencesininstitutionsandothereconomicstructureshaveimportantimplicationsforthetransmissionoftheECB’smonetarypolicy.Inparticular,structuralheterogeneitycancontributetocross-countrydifferencesinthere-sponsesofoutputandinflationtomonetarypolicydecisions(

Barigozzi,ContiandLuciani,

2014;

Ciccarelli,MaddaloniandPeydró,

2013;

Corsetti,DuarteandMann,

2022;

Slacalek,Tris-

taniandViolante,

2020

)

.1

Forinstance,economieswithstronginstitutionalqualityarelikelytobelessdependentonshort-termfinancialinflowsfromabroadandmaythereforebelessvulnerabletotighteningfinancialconditionsthancountrieswithweakerinstitutionalback-grounds.Suchcross-countryheterogeneitymaycontributetorealornominaldivergences,makingitlesslikelythatthecommonmonetarypolicyisalignedwitheconomicconditionsineachindividualeuroareacountry.

Inthispaper,weexploreifdifferencesininstitutionalqualityacrosseuroareacountriesalsomatterfortailrisksintheaftermathofmonetarypolicyshocks.Whenpolicymakersconsidertheimpactofmonetarypolicychangesonfutureeconomicactivity,theytypicallyfocusonthemostlikelyscenario,i.e.themeanofthe(conditional)distributionoffutureGDPgrowth.However,centralbanksalsoincreasinglyanalysetherisksaroundthecentralprojectioninquantitativeterms.Againstthisbackdrop,ourpaperaimstoshedlightontheroleofinstitutionalfactorsinshapingdownsideriskstoGDPgrowthintheaftermathofmonetarypolicyshocksinaheterogeneousmonetaryunion.

TocapturedownsideriskstofutureGDPgrowth,weusethegrowth-at-risk(GaR)frame-workproposedby

Adrian,BoyarchenkoandGiannone

(2019)

.Inlinewiththeliterature(see,e.g.,

FigueresandJaroci´nski

(2020)and

Gächter,GeigerandHasler

(2023)),wedefineGaRas

thelowestdecileofthedistributionofpredictedGDPgrowth,foragiventimehorizon,con-ditionalonasetofcurrenteconomicandfinancialconditions.OurGaRmeasureisderivedfromapanelquantileregression,usingtheestimatordevelopedby

MachadoandSantosSilva

(2019)

.Thesamplecoversall20euroareacountriesovertheperiod1999Q1-2019Q4.

Inasecondstep,weestimatethecausalimpactofmonetarypolicyshocksonGaRfol-lowingthemethodproposedby

Loria,MatthesandZhang

(2024)

.2

Monetarypolicyshocksareconstructedbasedonhigh-frequencymovementsinassetpricesaroundECBpolicyan-nouncementsandcleanedfromcentralbankinformationeffects(

Gürkaynak,SackandSwan-

1Takingabroaderperspective,Sondermann(2018)showsthattheoutputlosssufferedbyeuroareacountries

withweakereconomicstructuresinresponsetoacommonshock(notnecessarilyamonetarypolicyshock)ison

averagetwiceaslargeastheoutputlossofthebestperformers.

2WhiletheGaRliteraturetypicallydoesnotidentifythecausalimpactofstructuralshocksonGaR,Loria,

MatthesandZhang(2024)showthatcontractionaryUSmonetarypolicyshocksareamongthestructuralshocks

whichdisproportionatelyincreasetheriskoflargedownturnsintheUnitedStates.Beuteletal.(2022)showthat

theseshockscauseelevateddownsideriskstogrowtharoundtheworld.Wefollowthisapproachandestablish

causalitybetweenmonetarypolicyshocksandGaRintheeuroarea.

ECBWorkingPaperSeriesNo29895

son

(2005);

Altavillaetal.

(2019);

Jaroci´nskiandKaradi

(2020))

.WeusetheWorldBank’sWGIdata(

KaufmannandKraay,

2023)tosplitthesampleintoeuroareacountrieswithweakerand

strongerinstitutionalquality,respectively.ThisallowsustostudydifferencesintheimpulseresponsesofGaRtomonetarypolicyshocksbetweenthesetwocountrygroups.

WefindthatmonetarypolicyhasahigherimpactondownsideriskstoGDPgrowthintheshorttermthaninthemediumterm.However,thisaggregateresulthidessignificanthetero-geneityacrosscountries.Ineconomieswithweakinstitutionalquality,medium-termgrowthrisksincreasesubstantiallyfollowingcontractionarymonetarypolicyshocks.Incontrast,theserisksremainrelativelystableincountrieswithhighinstitutionalquality.Interestingly,expansionarymonetarypolicyshockshaveamoresymmetricimpactthancontractionaryshocks,bothacrosscountriesandquantilesoftheconditionalgrowthdistribution.

Inspectingthetransmissionchannels,wefindthatmedium-termrisksincreasethroughtheimpactthatmonetarypolicyshockshaveonvariablescapturingmacro-financialvulner-abilities—andthischannelismuchmorepronouncedforcountrieswithlowinstitutionalquality.Ourmainresultsarerobustto(i)usingdifferentindicatorscapturingmedium-termriskstoGDPgrowthwhenestimatingGaR,(ii)employingdifferentindicatorsofinstitutionalquality,(iii)accountingforcross-countrydifferencesinincomelevelsand(iv)alteringeitherthecountriesorthetimeperiodcoveredinthesample.

Ourresultshaveimportantpolicyimplications.First,ourempiricalfindingssuggestthatimprovinginstitutionalqualitycanstrengthentheeconomicresilienceofeuroareacountries.Insofar,wecomplementexistingstudiesthatemphasisetheroleofbankcapitalisation(

Aik-

manetal.,

2021),macroprudentialmeasuresormonetarypolicyinstruments(Galán,

2024)

insteeringGaR.Second,ourfindingsindicatethatinstitutionalconvergencewouldsupportthesmoothtransmissionofmonetarypolicybyensuringamorehomogeneousresponseofthetailofthemedium-termgrowthdistributiontomonetarypolicytightening.Thisaddsanimportantdimensiontothediscussionoffinancialstabilityconsiderationsintheconductofmonetarypolicy(

Bochmannetal.,

2023

).

Theremainderofthepaperisstructuredasfollows.Section

2

outlinesthemethodologyemployedtoestimateGaRandpresentstheresultingestimates.InSection

3

,wecomputeimpulseresponsesoftheGaRmeasurestomonetarypolicyshocksandexploretheroleofinstitutionalqualityinexplainingthecross-countryheterogeneityintheseimpulseresponses.Section

4

providesanoverviewofourrobustnesschecksandSection

5

concludes.

2Growth-at-riskandmacro-financialvulnerabilities

WestartouranalysisbyestimatingGaRoverdifferenttimehorizonsinasampleofeuroareacountries.Thisexerciseillustratestherelativeimportanceofdifferentmacro-financialvariablesfordownsideriskstogrowth,dependingonthetimehorizonconsidered.Weshowthatshort-termGaRestimatesforeuroareacountriesaremostlyassociatedwithfinancialstressindicators,whilemedium-termriskstogrowtharenotstronglycorrelatedwithcurrentfinancialstress.Instead,onlymacrovulnerabilitiesmatterformedium-termGaR.Ourfind-ingsthuspointtotwodifferentchannelsthroughwhichdownsideriskstoGDPgrowthmay

ECBWorkingPaperSeriesNo29896

materialise.

Buildingonourfirst-stageregression,Section

3

willexploretheroleofinstitutionalqualityindeterminingtheresponseofGaRtomonetarypolicyshocks.Thistwo-stepapproach,asfurtherexplainedinmoredetailinSection

3

,enablesustofocusontheeffectsofmonetarypolicythataretransmittedviatheconditioningvariablesinourfirst-stageregression.ThemethodologytherebyallowsustoidentifythechannelsthroughwhichinstitutionalfactorsshapetheimpactofmonetarypolicyonGaR.

2.1Methodologyanddata

Following

Adrianetal.

(2022),weestimatepanelquantileregressionsmakinguseoflocal

projectionmethods(

Jordà,

2005)sothatweareabletoestimatetheconditionalforecastof

GDPgrowthbothfortheshortterm(definedas4quartersahead)andthemediumandlongerterm(8and12quartersahead,respectively).Toestimateourmodel,wefollow

Machadoand

SantosSilva

(2019)whoderiveanestimatorofconditionalquantilesfromthecombinationof

alocationandascalefunction,whichisparticularlyusefulinapanelsettingwithcountryfixedeffects

.3

Following

MachadoandSantosSilva

(2019),theconditionalpredicteddistributionoffu

-tureGDPgrowth,foragivenquantileofDyi,t+h,willbegivenby

q,t,τ=(Dyi,t+hjxi,t)=i,τ+xi,t,τ∈(0,1).(1)

Inlinewithpreviousstudies(see,e.g.,

FigueresandJaroci´nski

(2020)and

Gächter,Geiger

andHasler

(2023)),weconsiderthe10thpercentileofpredictedGDPgrowthtobeourGaR

measure.WedefineDyit+hastheannualisedaveragegrowthrateofGDPbetweenquarterst

andt+h:Dyi,t+h=

Thevariablesincludedinxi,trefertofinancialstressindicatorsandmacro-financialvulner-abilities,whichhavebeenshowntocontainthemostrelevantinformationformedium-termGaRintheeuroarea(

Lang,RusnákandGreiwe,

2023)

.FinancialstressiscapturedbytheCountryLevelIndexofFinancialStress(CLIFS),introducedby

Duprey,KlausandPeltonen

(2017)basedon

Hollo,KremerandLoDuca

(2012)

.TheCLIFScoversmeasuresofstressinequity,bondandforeignexchangemarketsandtakesco-movementsinthesemarketsegmentsintoaccount.Turningtoindicatorsofmacro-financialvulnerabilities,andascommonintheGaRliterature,weincludeameasureofexcessivecreditgrowthoverthepasttwoyears.ForthatwerelyontheBIScredit-to-GDPgapandcalculateitscumulativedeviationoverthepre-vious8quartersfromitslong-runtrend.BoththeCLIFSandthecumulativedeviationfromthetrendofthecredit-to-GDPgaparestandardisedbytheircountry-specificstandarddevia-tions.Wealsoincludethegrowthrateinhousepricesoverthepast8quarters.Inaddition,tocapturebothpublicandexternalsectorvulnerabilitiesweincludethecyclically-adjustedbud-

3Thisapproachallowsthecountryfixedeffectstovaryacrossquantiles,suchthatαi,τ三αi+δiq(τ).Thiscontrasts,forexample,withthemethodproposedby

Canay

(2011)whichrestrictscountryfixedeffectstobe

invariantacrossquantiles.

4ForIreland,weusethemodifieddomesticdemandindicatorreleasedbythenationalstatisticalauthority.ComparedtoGDP,itislessaffectedbydatadistortionsarisingfromtheactivitiesofmultinationalenterprises.

ECBWorkingPaperSeriesNo29897

getbalanceandtheseasonally-adjustedcurrentaccountbalance.Finally,theeffectofoverallcurrenteconomicconditionsonfuturedownsiderisksiscapturedbyincludingeachcountry’sGDPasacontrolvariable,asiscommonintheliterature.

Oursamplecoversalleuroareacountriesinthetimeperiodfrom1999Q1to2019Q4,al-thoughsomevariablesarenotavailableforthefullobservationperiod

.5

GDPgrowthratesarehighlyleft-skewedduringthisperiodacrosscountriesasshowninAppendix

A.1.

Moreover,theunconditionallowerpercentilesofGDPgrowthshowsubstantialheterogeneityacrosscountries,muchmoresothanthemedianoftheunconditionalGDPgrowthdistribution(Fig-ure

8

).Inotherwords,someeuroareacountriesappeartobemoresusceptibletoweakgrowthoutcomesthanothers.Thisisdespitethefactthattheeuroareacountrieshavebeensubjecttoanumberofcommonshocksoverthisperiod.Thecross-countryheterogeneitythussuggestsaroleforcountrycharacteristicsinexacerbatingdownsideriskstogrowth.

2.2GaRestimates

WestartdocumentingourresultsbyshowingGaRestimatesfordifferenttimehorizons,to-getherwiththetimeseriesoftheircross-countryaverages

.6

Figure

1

suggeststhat,inlinewith

Adrian,BoyarchenkoandGiannone

(2019)and

Adrianetal.

(2022),thepredictedlower

tailofthegrowthdistributionismuchmorevolatilethanhigherquantiles

.7

Thismeansthatdownsideriskstogrowthvarymuchmoreovertimethanupsiderisks.Ourframeworkalsoappearstogiveanearlypredictionofthedownturnsandtroughsoftheglobalfinancialcri-sisin2008.Althoughthe4-quarter-aheadGaRmeasuredoesabetterjobinthisregard(seeAppendix

A.3

),itisstillinterestingthatthemedium-termmodelcansignaltheincreasingprobabilityofadownturnaroundtwoyearsbeforeitmaterialised.

Table

1

presentstheestimatedcoefficientsforthequantileregression,fordifferenttimehorizons

.8

Asnotedabove,ourpreferredmeasureofGaRisthe10thpercentileofpredictedGDPgrowth.Thereisastrongassociationbetweenfinancialconditionsandshort-termriskstogrowth.Atighteningoffinancialconditions,reflectedinanincreaseintheCLIFS,isasignificantpredictoroflargemacroeconomicdownturnsoverafour-quarterhorizon.Thein-formationcontentoffinancialstressregardingriskstogrowthdecreasesoverlongerhorizons(eightandtwelvequarters)reflectingthefactthatfinancialconditionsmayremainbuoyantuntilshortlybeforerisksmaterialise(

IMF,

2017

).Incontrast,incorporatinginformationonthecredit-to-GDPgapdoesnotaddexplanatorypowertoGaRintheshorttermbuthelpstocaptureriskstogrowthoverthemedium-andlonger-term(eightandtwelvequarters).Strongrisesinhouseprices,negativebudgetbalancesandnegativecurrentaccountbalancesalsosignalheightenedtailriskstogrowth,especiallyoverthelongerterm(or,atleast,insim-ilarmagnitudesforshorterandlongerhorizons,asopposedtoCLIFS).Thesefindingsonthe

5InAppendix

A.4.2

weshowthatthecoefficientsdonotsignificantlychangeifweextendthesampletoincludetheCOVID-19periodandthesubsequentyears.

6SeethefootnoteofFigure

1

foranexplanationofhowweobtainthisseries.

7Sinceweareinterestedincross-countryheterogeneityandtheroleofinstitutionalcharacteristicsinthetransmissionofmonetarypolicy,wefocusonmedium-termGaR.Figure

1

showsthecross-countryaverageof8-quarter-aheadGaR.InAppendix

A.3

weshowthesamefigureforothertimehorizons.

8Inappendix

A.4

weshowthatthesecoefficientsareverysimilaracrossasetofdifferentspecifications.Additionally,inappendix

A.2

weshowthecoefficientsforotherquantilesofthedistribution.

ECBWorkingPaperSeriesNo29898

Figure1:Predicted10thpercentile(GaR),medianand90thpercentileof8-quarter-aheadGDPgrowthandrealisedGDPgrowth

%

8

6

4

2

0

-2

-4

-6

-8

-10

-12

10thQuantile50thQuantile90thQuantileRealized

Mean

SD

10thperc.(GaR)-1.081.54Median1.770.88

90thperc.3.900.50

Realized1.932.48

2000q12005q12010q12015q12020q1

Quarter

Notes:Thepredicted8-quarter-ahead10thpercentile,medianand90thpercentileoftheannualisedaveragegrowthrateofGDParethecross-countryaveragesofeachcountryprediction(countryspecificpredictionsareobtainedwiththeestimatesofthepanelmodelofequation

1)

.Onceaveragedbyquarter,theseseriesareshiftedforwardby8quarterssuchthatthetimingofthepredictedgrowthrateandtherealisedoneforagivenquartermatch.

termstructureofGaRareinlinewithpreviousfindingsintheliterature,suchas

Adrianetal.

(2022)andinparticular

Lang,RusnákandGreiwe

(2023)whoshowthatonlymacro-financial

vulnerabilityindicatorsreflectingcreditandassetpriceimbalancescontaininformationaboutmedium-termGaRintheeuroarea.Therefore,weinterpretthisfindingasevidenceoftwokeychannelsbehindshort-termandmedium-termGaR:ashort-termchannelconnectedwithfinancialstressandamedium-termchannellinkedtomacro-financialvulnerabilities.

Itisalsointerestingtoanalysethetimevariationinthecontributionstodownsiderisksfromeachexplanatoryvariable.Figure

2

presentsthecontributionstoGaRfordifferenthori-zons.Figure

2a

illustratesthatweakfinancialandeconomicconditionsmakethelargestcontributiontodownsiderisksintheshort-term.ThereisasignificantcontributionofCLIFSaroundtheglobalfinancialcrisis,asonewouldexpect.However,Figure

2b

showsthatmacroe-conomicvulnerabilitiesweighstronglyonthepredictionofGaRoverlongerhorizons.Inpar-ticular,weakpublicfinancescontributedstronglytothelower10thpercentileofconditionalgrowtharoundthesovereigndebtcrisis.Figure

2c

confirmstheimportanceofmacro-financialvulnerabilitiesforGaRinthelongertermalsooverahorizonof12quarters.Atthesametime,thecontributionoffinancialstresstolonger-termriskstogrowthisnegligible.

ECBWorkingPaperSeriesNo29899

Figure2:AveragecontributionstoGaRforecast,h=4,h=8andh=12quartersahead

PercentagePoints

2

0

-2

-4

-6

-8

2000q12005q12010q12015q12020q1

(a)h=4

PercentagePoints

2

0

-2

-4

2005q1

2000q1

2010q1

2015q1

2020q1

(b)h=8

PercentagePoints

2

0

-2

-4

2000q1

2005q1

2010q1

2015q1

2020q1

二GDP

二CurrentAccount

Credit-to-GDPGapHousePrices

GaR

二CLIFS

BudgetBalance

(c)h=12

Notes:GaRreferstothe10thpercentileofpredictedGDPgrowth.ThepredictedGaRmeasuresplottedarethecross-countryaveragesoftheindividualcountrypredictions(thatwereobtainedusingmodel

1

),netofthecountryfixedeffectandthecoefficientofthedummyforwhenthecountryadoptedtheeuro.

ECBWorkingPaperSeriesNo298910

Table1:QuantileregressioncoefficientsfordifferenthorizonsofGaR

h=4

h=8

h=12

CLIFS

-0.780***

-0.331

-0.176*

(0.339)

(0.429)

(0.136)

GDP

0.318***

0.049

-0.004

(0.158)

(0.195)

(0.054)

Credit-to-GDPGap

-0.255

-0.525*

-0.435***

(0.316)

(0.497)

(0.164)

HousePrices

-0.040*

-0.039

-0.031***

(0.035)

(0.050)

(0.015)

BudgetBalance

0.441***

0.438**

0.314***

(0.175)

(0.262)

(0.088)

CurrentAccount

0.279***

0.228*

0.247***

(0.094)

(0.142)

(0.048)

Observations

1179

1103

1027

Notes:GaRreferstothe10thpercentileofpredictedGDPgrowth.Standarderrorsinparenthesis.Quantileregressionwithcountryfixedeffectsandcontrollingforthetimingofeuroadoption.Starsindicatesignificanceat*p<0.32,**p<0.10,***p<0.05.

3Impactofmonetarypolicyshocksongrowth-at-risk

ThissectionlooksattheimpactofmonetarypolicyshocksonGaRinaheterogeneousmon-etaryunion.Morespecifically,weanalysetheextenttowhichcross-countrydifferencesininstitutionalqualityaffecttheresponseofGaRtoamonetarypolicyshockintheeuroarea.Indoingso,wetrytodisentangletherelevanceoffinancialconditionsandmacroeconomicvulnerabilities,respectively,astransmissionchannels.Inaddition,weexplorepossiblenon-linearitiesinthesetransmissionchannelsdependingonwhetherthemonetarypolicyshockiscontractionaryorexpansionary.

3.1Methodologyanddata

Following

Loria,MatthesandZhang

(2024),weassesstheresponseoftheGaRvaluespre

-

dictedinthefirst-stageregression(seeSection

2.1

)tomonetarypolicyshocks.Definingq,t+s,τ

as