bank financing for smes from 2014 to 2019: effect of
TRANSCRIPT
1NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Bank financing for SMEs from 2014 to 2019 : effect of changes in the law on lending
Ch. PietteM.-D. Zachary
Introduction
TheβLawβofβ21 December 2013,βamendedβbyβtheβLawβofβ21 December 2017,βaimsβtoβfacilitateβaccessβtoβbankβfinanceβforβsmallβandβmedium-sizedβenterprisesβ(SMEs).βToβthatβend,βitβcomprisesβaβrangeβofβprovisionsβintendedβtoβ correctβ anyβ informationβ asymmetriesβwhichβ couldβ affectβ theβ commercialβ relationshipβbetweenβ lendersβ andβborrowersβbyβspecifyingβtheβtransparencyβobligationsβincumbentβonβbothβparties.βThus,βfirmsβapplyingβforβcreditβhaveβ toβ supplyβ sufficientlyβ exhaustiveβ dataβ onβ theirβ financialβ statements,β amongβ otherβ things.β Forβ theirβ part,βlendersβmustβmakeβ availableβ toβ applicantsβ standardβ documentsβ correspondingβ toβ theβ variousβ formsβ ofβ creditβwhichβ theyβ offer 1.β Inβ addition,β aβ proposedβ creditβ agreementβ hasβ toβ beβ accompaniedβ byβ aβ briefβ informationβdocumentβ settingβoutβallβ theβcharacteristicsβofβ theβcontract.βTheβ lenderβmustβalsoβascertainβ theβmostβ suitableβtypeβofβcredit,βtakingβaccountβofβtheβfirmβsβfinancialβpositionβatβtheβtimeβofβconclusionβofβtheβcontractβandβtheβpurposeβofβtheβcredit.βTheseβprovisionsβaimβinβparticularβtoβstimulateβcompetitionβbetweenβcreditβinstitutionsβbyβmakingβitβeasierβforβfirmsβtoβcompareβtheβvariousβoptionsβavailableβtoβthem.βInβtheβeventβofβaβrefusalβtoβgrantβcredit,βtheβlenderβmustβinformβtheβfirmβofβtheβessentialβreasonsβforβthatβrefusalβorβtheβfactorsβinfluencingβtheβriskβassessment,βandβthatβinformationβmustβbeβtransparentβandβcomprehensibleβforβtheβfirm.βTheβlawβalsoβincludesβanβ articleβ onβ theβ rulesβ forβ fixingβ theβ prepaymentβ penaltyβ whichβ theβ lenderβ isβ entitledβ toβ demandβ fromβ anyβborrowersβwhoβdecideβtoβrepayβtheirβloanβearly.βTheβloanβagreementβmustβexplicitlyβmentionβtheβamountβofβtheβpenalty.βTheβlatterβisβdeterminedβbyβaβstandardisedβprocedureβaccordingβtoβaβmethodβofβcalculationβdefinedβinβaβcodeβofβconduct,βrevisedβin 2018βfollowingβchangesβtoβtheβlawβandβagreedβbetweenβemployersββorganisationsβrepresentingβtheβinterestsβofβself-employedβworkersβ(theβSNI)βandβSMEsβ(UnizoβandβtheβUCM),βonβtheβoneβhand,βandβFebelfinβonβtheβother.βForβloansβforβanβinitialβsumβofβnoβmoreβthanββ¬β2 million,βtheβpenaltyβmustβnotβexceedβsixβmonthsββ interest.βAtβtheβtime,βtheβfinancialβsectorβhadβexpressedβcertainβreservationsβonβthisβ lastβprovision,βraisingβconcernsβofβaβrelativeβincreaseβinβtheβinterestβrateβonβloansβtoβSMEs.
Theβ lawβ alsoβ statesβ thatβ itsβ ownβ effectsβmustβ beβ reviewedβ everyβ twoβ years.β Inβ thatβ connection,β theβNationalβBankβisβaskedβtoβgiveβaββpreliminaryβopinionβ 2.βTheβRoyalβDecreeβofβ10 April 2016,βwhichβlaysβdownβtheβreviewβprocedures,βalsoβspecifiesβthatβtheβBankβshallβprovideβstatisticalβdataβonβtheβloansβconcernedβbyβtheβlaw.
1β Since 2018,βthisβrequirementβnoβlongerβappliesβtoβloansβofβlessβthanββ¬β25,000,βprovidedβtheyβdoβnotβcompriseβaβpenaltyβclauseβandβareβnotβcoveredβbyβcollateralβorβguarantees.
2 AnβopinionβisβalsoβrequestedβfromβtheβFSMA,βtheβfinancialβdisputeβmediatorβOMBUDSFIN,βandβtheβorganisationsβrepresentingβsmallβbusinesses.
2NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Theβaimβofβ thisβarticleβ isβ toβ summariseβandβcommentβonβ theβvariousβdataβ consideredβ relevantβ forβmonitoringβlendingβ toβSMEsβbyβ residentβbanks,βnamelyβdataβonβ theβvolumeβofβ suchβ loans,β theβ interestβ ratesβappliedβandβtheβconditionsβforβgrantingβloans.βHowever,βtheβexaminationβofβtheseβdataβisβnotβsufficientβinβitselfβtoβdetermineβwhetherβ structuralβ effectsβ emergedβ followingβ theβ lawβsβ entryβ intoβ forceβ;β itβ isβ thereforeβ supplementedβ byβ anβeconometricβanalysis,βwhichβisβsummedβupβinβtheβsecondβpartβofβtheβarticle.βFinally,βtheβconclusionβdiscussesβanyβeffectsβofβtheβlawβonβtheβdynamicsβofβlendingβtoβSMEs.
Whenβthisβarticleβwasβwritten,βtheβdataβtakenβintoβaccountβforβtheβanalysisβwereβavailableβupβtoβtheβendβof 2019βforβsomeβvariablesβandβupβtoβFebruary 2020βforβothers,βi.e.βbeforeβtheβeruptionβofβtheβCovid-19 crisis.βThatβcrisisβisβliableβtoβresultβinβfundamentalβchangesβinβtheβsituationβfromβtheβpointβofβviewβofβbothβcreditβinstitutionsβandβfirms.βTheβconclusionsβdrawnβatβ theβendβofβ thisβ studyβwillβ thereforeβneedβ toβbeβ reassessedβ inβaβ fewβmonthsββtime.βMeanwhile,βweβreferβinterestedβreadersβtoβtheβspecificβmonitoringβbyβtheβBankβconductsβpublishedβonβitsβwebsiteβasβpartβofβtheβObservatoryβforβCreditβtoβNon-financialβCorporations 1.
1. Recent developments in business credit
1.1 Volume of bank loans
Lendingβ byβ residentβ creditβ institutionsβ toβ residentβ non-financialβ corporationsβ isβ usuallyβ monitoredβ viaβ twoβstatisticalβ sources,βnamelyβ theβdataβobtainedβ fromβbankβbalanceβ sheetsβ andβ thoseβobtainedβ fromβ theβCentralβCorporateβCreditβRegister.βInβprinciple,βtheβfirstβofβtheseβtwoβsourcesβisβexhaustive,βbutβitβhasβtheβdisadvantageβofβonlyβprovidingβinformationβatβanβaggregateβlevel.βThisβdefectβisβovercomeβbyβtheβsecondβsource,βwhichβpermitsβaβbreakdownβofβtheβstockβofβbusinessβloansβaccordingβtoβtheβfirmsββsizeβandβtheirβbranchβofβactivity.βTheseβstatisticsβareβcompiledβusingβseparateβcollectionβmethodsβandβareβbasedβonβdifferentβdefinitions 2β:βnaturally,βthatβgivesβriseβtoβsomeβstatisticalβvariations.βItβshouldβalsoβbeβpointedβoutβthatβtheβanalysisβconcernsβcreditβusedβbyβfirmsβtakenβintoβ accountβ inβ theβCentralβ Creditβ Register 3β andβ focusesβ onβ non-financialβ corporations,β thusβ excludingβ firmsβclassifiedβinβtheβfinancialβservicesβbranch 4.
Nonetheless,βtheseβtwoβsources 5βprovideβaβpictureβwhichβtalliesβoverallβwithβdevelopmentsβinβoutstandingβbankβloansβtoβfirmsβlocatedβinβBelgium.βAsβillustratedβinβchart 1,βthoseβdevelopmentsβclearlyβbearβtheβmarksβofβcrisisβepisodesβ:βfirst,β theβgreatβ recessionβof 2008 and 2009,βandβ thenβ theβ sovereignβdebtβ crisisβwhichβaffectedβ theβeuroβ areaβ in 2011 and 2012.β Theseβ twoβ eventsβ hadβ aβ negativeβ impactβ onβ bothβ demandβ forβ loansβ andβ theβsupplyβofβcredit.βLaggingβsomewhatβbehindβtheβtrendβinβeconomicβactivity,βtheβvolumeβofβcreditβusedβbyβfirmsβhasβthusβcontractedβonβtwoβoccasionsβinβtheβpastβtenβyearsβ:βfirst,βbetweenβmid-2009 andβmid-2010,βandβthenβin 2013 or 2014,βdependingβonβtheβdataβsourceβconsidered.βAfterβthat,β itβbeganβrisingβagainβtowardsβtheβendβof 2014,βandβthatβexpansionβacceleratedβfrom 2015βtoβmid-2018,βbeforeβslowingβdownβupβtoβFebruary 2020.
However,βtheβmovementsβinβtheβtotalβvolumeβofβloansβmaskβhighlyβheterogeneousβvariationsβaccordingβtoβfirmβsize.β Inβ reality,β theβ procyclicalβ characterβ ofβ theβ trendβ inβ creditβ primarilyβ concernsβ lendingβ toβ largeβ firms.β Theβreductionβinβtheβtotalβvolumeβofβloansβduringβtheβtwoβcrisisβperiodsβisβalmostβentirelyβattributableβtoβthem.βTheβ
1β https://www.nbb.be/doc/dq/kredobs/fr/ko_home.htm.2 First,βtheβCentralβCorporateβCreditβRegisterβisβconstantlyβupdatedβbyβcreditβinstitutions,βbutβtheβdataβmayβbeβrevisedβretrospectivelyβduringβtheβtwelveβmonthsβfollowingβtheirβsubmission.βThatβimpliesβthatβtheβexistingβstatisticsβforβtheβperiodβfromβMarch 2019βtoβFebruary 2020βmayβyetβbeβamended.βNext,βsomeβtransactionsβregardedβasβloansβunderβSchemeβAβareβexcludedβfromβtheβCentralβCorporateβCreditβRegisterβsβstatisticsβ:βthatβapplies,βforβinstance,βtoβclaimsβresultingβfromβrepoβtransactionsβinβsecurities.βFinally,βtheβcriteriaβforβgrantingβloansβviaβanβassociationβorβsyndicateβtoβaβcounterpartyβwithinβtheβassociationβareβnotβtheβsame,βasβtheβbanksβhaveβdirectβinformationβwhichβisβnotβcurrentlyβpassedβonβtoβtheβCentralβCorporateβCreditβRegisterβ;βthatβsometimesβleadsβtoβdifferencesβinβgeographicalβandβ/βorβsectoralβclassification.
3β Creditβusedβisβdifferentβfromβcreditβauthorised,βwhichβconcernsβcreditβlinesβmadeβavailableβtoβfirmsβbutβnotβnecessarilyβusedβinβfull.4β Moreβspecifically,βthisβconcernsβtheβbranchesβunderβNACEβcodeββKβ.5β TheβperiodβcoveredβendsβinβFebruary 2020βowingβtoβtheβavailabilityβofβdataβatβtheβtimeβofβconcludingβthisβanalysis.
3NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
volumeβofβbankβloansβcontractedβbyβsmallβfirms 1βshowsβmuchβmoreβstableβgrowth.βThereβhaveβbeenβtwoβdistinctβphasesβinβrecentβyearsβ:β(1)βanβaccelerationβinβtheβcreditβexpansionβrateβfrom 2015βtoβtheβendβof 2017,β(2)βfollowedβbyβaβdecelerationβwhichβpersistedβupβtoβFebruary 2020β(latestβavailableβdata).βDuringβthatβsameβperiod,βtheβvolumeβofβloansβtoβmedium-sizedβfirmsβdeclinedβforβ12 consecutiveβmonths,βbetweenβDecember 2016βandβNovember 2017,βwhileβlendingβtoβlargeβfirmsβremainedβpositiveβalmostβthroughoutβtheβperiodβfrom 2015βtoβFebruary 2020.
Overall,βsinceβtheβbeginningβof 2014βββi.e.βsinceβtheβentryβintoβforceβofβtheβLawβofβ21 December 2013 onβSMEβfinancingβββ theβvolumeβofβ creditβusedβbyβmicroβcompaniesβandβ smallβfirmsβ takenβasβaβwholeβhasβ risenβbyβanβannualβ averageβofβ4.6β%β (similarβ toβ theβ riseβduring 2008-2013 whenβannualβgrowthβaveragedβ4.8β%β forβ thisβcategoryβ ofβ firms),βwhileβ lendingβ toβmedium-sizedβ firmsβ expandedβ byβ 0.6β%β (seeβ chart 2).β Althoughβ lendingβtoβmedium-sizedβfirmsβdidβ increase,β theβgrowthβ rateβwasβmuchβ lowerβ thanβ inβ theβ sixβprecedingβ yearsβ (2008-2013),βwhenβ itβaveragedβ7.6β%.βLendingβtoβ largeβfirmsβexpandedβbyβanβaverageβofβ4.2β%βbetween 2014βandβFebruary 2020 2,βfollowingβaβ1.3β%βriseβbetween 2008 and 2013.
1β Inβthisβstudy,βtheβtermββsmallβfirmsββincludesβbothβmicroβcompaniesβ(usingβtheβmicroβmodelβforβannualβaccounts)βandβsmallβfirmsβ(filingβaccountsβinβtheβabbreviatedβformat).
2 Inβaddition,βcreditβdevelopmentsβmayβvaryβgreatlyβfromβoneβbranchβofβactivityβtoβanotherβ(seeβannexβ2).βForβexample,βinβtheβcaseβofβsmallβfirms,βbankβlendingβexpandedβfasterβbetween 2014βand 2020βinβtheβenergyβsector,βrealβestateβactivitiesβandβtheβconstructionβindustry.βConversely,βgrowthβwasβslowerβinβtheβwholesaleβandβretailβtradeβsector.βAnnexesβ1 andβ2 provideβdetailedβstatisticsβonβcreditβdevelopmentsβinβtheβvariousβbranchesβofβactivityβandβforβtheβdifferentβfirmβsizeβcategories.
Chart 1
Growth rate of bank lending to firms
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Bank loans according to credit institution balance sheets 1 and the Central Corporate Credit Register Β²(in %)
Growth rate of used credit: breakdown by firm size 3
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Central Corporate Credit Register 2
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Medium-sized firms
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Sourceβ:βNBBβ(CentralβCorporateβCreditβRegisterβandβcreditβinstitutionβbalanceβsheets).1β Sumβofβflowsβoverβ12 monthsβinβrelationβtoβtheβoutstandingβtotalβrecordedβaβyearβearlier.2 Movingβaverageβoverβ4 quartersβofβtheβgrowthβrateβoverβthreeβmonths,βwhichβisβthenβannualised.βNoβaccountβwasβtakenβofβcreditβusedβbyβ
firmsβclassifiedβinβtheβfinancialβservicesβsector.3β Firmsβusingβmicroβmodelsβforβtheirβannualβaccountsβareβconsideredβtoβbeβmicroβcompaniesβandβfirmsβsubmittingβanβabbreviatedβmodelβ
areβconsideredβtoβbeβsmallβfirms.βFirmsβsubmittingβfull-formatβaccountsβareβregardedβasβlargeβorβmedium-sizedβaccordingβtoβwhetherβtheyβexceedβoneβorβmoreβofβtheβthresholdsβdefinedβinβtermsβofβtheβnumberβofβworkersβ(50 FTE),βturnoverβ(β¬β9β000 000)βandβbalanceβsheetβtotalβ(β¬β4β500 000). Forβmoreβdetails,βseeβhttps://www.nbb.be/doc/ba/infomail/mail_f_50.pdf.
4NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
However,β itβ shouldβ beβ notedβ that,β inβ viewβ ofβ theβ procyclicalityβ ofβ theβ statisticalβ seriesβ describedβ here,β andβmoreβspecificallyβthoseβconcerningβlargeβfirms,βdescriptiveβdataβcannotβbeβusedβonβtheirβownβtoβinterpretβtheseβdevelopmentsβ asβ beingβ genuinelyβ specificβ toβ theβ periodβ inβwhichβ theβ lawβ hasβ applied.β Forβ thatβ purpose,βweβconductedβaβmoreβdetailedβanalysisβusingβanβeconometricβmethodβwhichβenablesβusβtoβdistinguishβbetweenβtheβeffectsβ specificβ toβ thisβperiodβandβ thoseβ relatingβ toβ theβchangesβ inβ theβeconomicβenvironmentβandβborrowingβrates.βTheβdetailsβareβsetβoutβinβtheβsecondβpartβofβthisβarticle.
1.2 Interest rates charged by credit institutions
Theβinterestβratesβchargedβbyβbanksβgrantingβcreditβtoβnon-financialβcorporationsβcanβbeβtrackedβviaβtheβresultsβofβtheβMIRβ(MonetaryβfinancialβinstitutionβInterestβRate)βsurvey.βChart 3 presentsβtheβfourβmainβseriesβestablishedβonβtheβbasisβofβthatβsurvey.βTheyβareβcalculatedβasβweightedβaveragesβofβvariousβtypesβofβinterestβrates 1,βnamelyβvariableβorβshort-termβratesβββforβloansβofβlessβthanβorβmoreβthanββ¬β1 millionβ(i.e.βloansβwithβanβinitialβfixed-rateβperiodβofβ lessβ thanβoneβyear),βmedium-termβ interestβ ratesβ initiallyβfixedβ forβaβperiodβofβbetweenβoneβandβfiveβyears,βandβfinally,βlong-termβratesβinitiallyβfixedβforβmoreβthanβfiveβyears.βInβBelgium,βtheβmedium-βandβlong-termβweightedβaverageβ interestβ ratesβareβonlyβestablishedβforβsumsβofβ lessβ thanββ¬β1 millionβ (becauseβtheβvolumeβofβtheseβmaturitiesβisβtooβsmallβforβamountsβinβexcessβofββ¬β1 million).
Chart 2
Annualised growth rate of used credit : breakdown by firm size 1, 2, 3
(inβ%)
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Micro companies and small firms Medium-sized firms Large firms Total
Sourceβ:βNBBβ(CentralβCorporateβCreditβRegister).1β Noβaccountβwasβtakenβofβcreditβusedβbyβfirmsβclassifiedβunderβfinancialβservices.2 Theβannualisedβgrowthβrateβisβdeterminedβasβfollowsβ:βanβaverageβisβcalculatedβonβtheβbasisβofβtheβquarterlyβgrowthβratesβ/βoverβaβthree-monthβperiodβ(fromβJanuary 2013,βtheβdateβfromβwhichβmonthlyβdataβareβavailable).β(EncoursβTβ/βEncoursβT-1
-1),βdisregardingβtheβquartersβaffectedβbyβaβbreakβinβtheβseriesβ(namelyβtheβsecondβquarterβof 2012,βandβtheβfourthβquarterβof 2014).βThisβaverageβisβthenβannualised.
3β Firmsβusingβmicroβmodelsβforβtheirβannualβaccountsβareβconsideredβtoβbeβmicroβcompaniesβandβfirmsβsubmittingβanβabbreviatedβmodelβareβconsideredβtoβbeβsmallβfirms.βFirmsβsubmittingβfull-formatβaccountsβareβregardedβasβlargeβorβmedium-sizedβaccordingβtoβwhetherβtheyβexceedβoneβorβmoreβofβtheβthresholdsβdefinedβinβtermsβofβtheβnumberβofβworkersβ(50 FTE),βturnoverβ(β¬β9β000 000)βandβbalanceβsheetβtotalβ(β¬β4β500 000). Forβmoreβdetails,βseeβhttps://www.nbb.be/doc/ba/infomail/mail_f_50.pdf.
1β Forβmoreβdetailsβonβtheβmethodology,βgoβtoβhttps://www.nbb.be/doc/dq/mir/pdf/facteurs_en.pdf.
5NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Chart 3
MIR rates and OIS rates 1 : breakdown according to the initial fixation period(inβ%)
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GapBorrowing rate 1-year OIS rate GapBorrowing rate 1-year OIS rate
GapBorrowing rate 3-year OIS rate GapBorrowing rate 5-year OIS rate
Loans of less than β¬ 1 million, at fixed interest rates, with an initial fixation period of between 1 and 5 years
Loans of less than β¬ 1 million, at fixed interest rates, with an initial fixation period of more than 5 years
Loans of less than β¬ 1million, at variable interest rates, with an initial fixation period of less than 1 year
Loans of more than β¬ 1 million, at variable interest rates, with an initial fixation period of less than 1 year
Sourcesβ:βNBBβ(MIRβsurvey),βEikon.1β OvernightβindexβswapβββOISβ:βtheβinterestβratesβonβloansβtoβprimeβbanks,βandβconsequentlyβanβapproximationβofβtheβratesβatβwhichβbanksβcanβraiseβfinance.βInterestβratesβonβshort-termβloansβwereβcomparedβwithβtheβ1-yearβOIS,βratesβonβmedium-termβloansβwereβcomparedβwithβtheβ3-yearβOISβandβratesβonβlong-termβloansβwereβcomparedβwithβtheβ5-yearβOIS.
6NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Theβ movementβ inβ theseβ interestβ ratesβ largelyβ reflectsβ theβ variousβ monetaryβ policyβ measuresβ adoptedβ inβ theβEurosystem.βInβaccordanceβwithβtheβtasksβentrustedβtoβitβbyβtheβTreatyβonβtheβFunctioningβofβtheβEuropeanβUnion,βtheβEurosystemβsβprimaryβaimβ isβ toβmaintainβpriceβstability.βTheβpolicyβ interestβ ratesβsetβbyβ theβECBβGoverningβCouncilβthereforeβgenerallyβmirrorβinflationβexpectations.βTheseβwereβrevisedβdownwardsβfollowingβtheβeruptionβofβtheβeconomicβandβfinancialβcrisisβin 2008,βasβwasβtheβoutlookβforβdemand.βTheβGoverningβCouncilβthereforeβmadeβsubstantialβcutsβ inβtheβpolicyβinterestβratesβfromβthenβon.βMoreover,β inβsettingβitsβdepositβfacilityβ interestβratesβatβaβnegativeβ levelβ fromβJune 2014,βandβestablishingβ itsβextendedβsecuritiesβpurchaseβprogrammeβatβ theβbeginningβof 2015,βtheβEurosystemβtriedβtoβencourageβportfolioβrebalancingβinβfavourβofβlendingβand,βultimately,βtheβfinancingβofβtheβrealβeconomy.βAlthoughβtheβassetβpurchaseβprogrammeβwasβsuspendedβinβDecember 2018,βitβwasβresumedβinβNovember 2019βinβorderβtoβreinforceβtheβaccommodativeβeffectsβofβtheβinterestβrates.βInβaddition,βtheβtargetedβlonger-termβrefinancingβoperationsβ(TLTRO)βalsoβhelpedβtoβboostβbankβlendingβtoβbusinessesβ(andβconsumers)βinβtheβeuroβarea,βandβsimilarlyβinβBelgium.βTheseβmeasuresβmaintainedβfavourableβcreditβconditionsβinβtheβeuroβarea.βTheβfirstβseriesβofβtargetedβlonger-termβrefinancingβoperations,βintendedβtoβstimulateβlendingβtoβeuroβareaβcreditβinstitutions,βwasβannouncedβinβJune 2014βandβextendedβoverβaβ2-yearβperiod.βTheβsecondβseriesβofβoperationsβ(TLTROβII)βbeganβinβMarch 2016,βandβtheβthirdβseriesβ(TLTROβIII)βinβMarch 2019.
TheseβvariousβmeasuresβconsiderablyβreducedβtheβfundingβcostsβofβcreditβinstitutionsβinβBelgiumβandβinβtheβeuroβareaβasβaβwhole.βChart 3 showsβtheseβcostsβaccordingβtoβ theβOISβ rates 1β forβmaturitiesβsimilarβ toβ thoseβofβ theβweightedβ averageβ interestβ ratesβ obtainedβ fromβ theβMIRβ survey.β Theβdeclineβ inβ fundingβ costs,βwhichβoccurredβmainlyβfromβtheβsecondβhalfβof 2008,βwasβnotβentirelyβreflectedβinβtheβborrowingβratesβthatβBelgianβbanksβofferedβtoβresidentβenterprises.βTheβgapβbetweenβtheseβtwoβtypesβofβinterestβratesβwidenedβaβlittleβfurtherβfollowingβtheβnewβcutsβinβkeyβinterestβratesβintroducedβfrom 2011.βHowever,βinβtheβcaseβofβloansβatβvariableβinterestβratesβ(withβanβinitialβfixationβperiodβofβlessβthanβoneβyear),βthatβgapβstabilisedβfrom 2012 atβaβlevelβcloseβtoβ2β%.βFurthermore,βtheβgapβisβhardlyβanyβgreaterβforβloansβofβlessβthanββ¬β1 millionβcomparedβtoβloansβofβmoreβthanββ¬β1 million,βwhichβsuggestsβthatβloansβforβsmallerβamountsβββtypicallyβsoughtβbyβSMEsβββwereβnotβaffectedβbyβaβhigherβinterestβrateβowingβtoβtheβlimitβonβprepaymentβpenalty.
Medium-βandβlong-termβfixedβinterestβratesβdeclinedβbyβmoreβthanβtheβinterbankβratesβin 2012 and 2013,βcausingβaβsimilarβreductionβinβtheβgapβinβrelationβtoβtheβcorrespondingβOISβrate.βSpreadβwithβrespectβtoβtheβOISβcontinuedβtoβ fall,β reachingβaβ lowβpointβ in 2017βbeforeβedgingβbackβupβ in 2018βand 2019.βDuring 2019,βmedium-βandβlong-termβinterestβratesβfellβ toβhistoricallyβ lowβlevels,β respectivelyβdecliningβtoβ1.20β%β(inβApril)βandβ1.46β%β(inβOctober),βindicatingβparticularlyβfavourableβcreditβconditionsβforβbusinesses.
1.3 Credit conditions
Apartβ fromβ theβ accommodativeβmonetaryβ policy,β otherβ factorsβ alsoβ contributedβ toβ theβ cutsβ inβ interestβ ratesβchargedβbyβbanks,βandβmoreβgenerally,βtheβeasingβofβtheβconditionsβtoβbeβmetβbyβfirmsβseekingβtoβborrow.βAsβwellβasβinterestβratesβandβcommercialβmargins,βtheseβconditionsβincludeβmaximumβloanβamountsβ/βloanβperiods,βasβwellβasβtheβcollateralβrequiredβandβnon-interestβrateβcharges.
Theseβconditionsβwereβ tightenedβ initiallyβ in 2008,βwhenβ theβeconomicβandβfinancialβ crisisβ erupted,βandβagainβin 2011-2012 atβtheβtimeβofβtheβsovereignβdebtβcrisisβ;βthisβappliedβtoβbothβSMEsβandβlargeβfirms.βAccordingβtoβtheβresultsβofβtheβBankβLendingβSurveyβcoveringβtheβfourβlargestβcreditβinstitutionsβoperatingβinβBelgium,βtheirβsupplyβofβ loansβbecameβmoreβ limitedβ in 2008 owingβ toβ theβ risksβ (whichβ theyβ sawβasβhavingβ increasedβconsiderably),βhigherβfundingβcostsβandβtighterβbalanceβsheetβconstraints.βThisβlastβaspectβwasβconnectedβwithβtheβprocessβofβbalanceβsheetβconsolidationβandβsizeβreductionβonβwhichβtheβbanksβhadβembarkedβafterβtheβstartβofβtheβcrisis.
1β Overnightβindexβswapβ(OIS).βThisβisβtheβinterestβrateβonβloansβtoβprimeβbanks.
7NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Betweenβ theβ endβ of 2013 andβ mid-2018,β theβ successiveβ keyβ interestβ rateβ cutsβ andβ otherβ monetaryβ policyβmeasuresβdidβmuchβtoβencourageβbanksβtoβeaseβtheirβloanβcriteria.βFurthermore,βtheβupturnβinβeconomicβactivityβin 2014βand 2015βledβtoβaβdownwardβrevisionβofβtheβassessmentβofβcreditβrisk.βFinally,βtheβresultsβofβtheβbankβsurveysβprimarilyβrevealβanβincreaseβinβtheβpressureβfromβcompetitionβbetween 2015βand 2018,βpromptingβtheβbanksβ toβ offerβmoreβ favourableβ creditβ conditionsβ forβ borrowers.β Theseβ developmentsβ areβ notβ specificβ toβ theβBelgianβmarketβ;βaβsimilarβpictureβalsoβemergedβinβotherβeuroβareaβcountries.βIn 2019,βtheβbanksβbeganβtoβtightenβtheirβcreditβconditionsβforβbothβlargeβfirmsβandβSMEs,βmainlyβowingβtoβtheβdeterioratingβriskβperceptionβinβtheβwakeβofβtheβslowdownβinβeconomicβactivity.
TheβgenerallyβfavourableβcharacterβofβcreditβconditionsβforβSMEsβinβrecentβyearsβisβconfirmedβbyβsurveysβamongβentrepreneurs,βsuchβasβtheβSurveyβonβtheβAccessβtoβFinanceβofβEnterprisesβ(SAFE)βconductedβeveryβsixβmonthsβinβtheβeuroβareaβcountries.βTheβresultsβofβthatβsurveyβ(presentedβinβchart 5)βinβfactβsuggestβaβmarkedβreductionβinβtheβobstaclesβtoβaccessβtoβbankβloansβbetweenβtheβfirstβhalfβof 2017βandβinβlimitedβapprovalsβtheβfirstβhalfβof 2018,βinitiallyβreflectedβinβaβdeclineβinβrejectionβratesβaccompanied,βfrom 2017,βbyβaβreductionβinβlimitedβapprovalsβofβloanβapplications.βAccordingβtoβtheβlatestβdata,βtheβobstaclesβareβstillβrelativelyβlowβinβhistoricalβterms,βalthoughβtheyβhaveβincreasedβsinceβmid-2018.
Chart 4
Credit standards applied to non-financial corporations : breakdown by firm size(weightedβnetβpercentages 1)
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β100
β80
β60
β40
β20
0
20
40
60
80
100
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β100
β80
β60
β40
β20
0
20
40
60
80
100
Cost of funds and balance sheet constraints 1
Trend in credit standardsPressure from competition 1
Risk perception 1
Risk tolerance Β²
Expected trend in credit standards
Large firms SMEs
Sourceβ:βNBBβ(Bankβlendingβsurvey).1β Aβpositiveβ(negative)βpercentageβcorrespondsβtoβtighteningβ(easing)βofβcreditβconditionsβorβaβfactorβcontributingβtoβtheβtighteningβ(easing)βofβthoseβconditions.
2 Factorβincludedβforβtheβfirstβtimeβinβtheβsurveyβinβtheβfirstβquarterβof 2015.
8NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Chart 5
Obstacles impeding access to bank financing for SMEs ΒΉ, Β²(percentagesβofβrespondents)
2010βS1
2010βS2
2011βS1
2011βS2
2012βS1
2012βS2
2013βS1
2013βS2
2014βS1
2014βS2
2015βS1
2015βS2
2016βS1
2016βS2
2017βS1
2017βS2
2018βS1
2018βS2
2019βS1
2019βS2
0
5
10
15
20
25
Did not apply because of possible rejection
Total
Received only a limited part of the amount requested
Refused because the cost was too high
Application rejected
Sourceβ:βECBβ(SAFE).1β Fewerβthanβ250 workers.2 Proportionβofβfirmsβnotβapplyingβforβbankβcreditβbecauseβofβpossibleβrejection,βorβapplyingβforβaβloanβbutβonlyβreceivingβaβlimitedβpartβofβtheβamountβrequested,βrefusingβcreditβbecauseβtheβcostβwasβtooβhigh,βorβhavingβtheirβapplicationβrejected.
Chart 6
Firmsβ assessment of conditions governing access to credit : breakdown by firm size ΒΉ(balanceβofβtheβpercentagesβofβfavourableβ(+)βandβunfavourableβ(β)βresponses,β4-quarterβmovingβaverage)
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β30
β20
β10
0
10
20
30
40
50
Small firms
Large firms
Medium-sized firms
Very large firms Sourceβ:βNBBβ(Quarterlyβsurveyβofβcorporateβcreditβconditions).1β Smallβ=β1-49 workersβ;βmediumβ=β50-249 workersβ;βlargeβ=β250-499 workersβ;βveryβlargeβ=β500 workersβorβmore.
9NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Theβ improvementβ inβ creditβ conditionsβ isβ likewiseβ borneβ outβ byβ theβ quarterlyβ surveyβwhichβ theβNationalβ BankβconductsβonβaβsampleβofβBelgianβentrepreneursβ(seeβchart 6).βTheβresultsβofβthatβsurveyβshowβthatβtheβimprovementβwhichβbeganβin 2013 andβpersistedβupβto 2016β(sinceβwhen,βentrepreneursββsatisfactionβhasβgenerallyβstabilisedβatβhistoricallyβhighβ levels)β concernedβbothβSMEsβandβ largeβfirms.βThatβ improvementβwasβalsoβapparentβ inβ theβvariousβaspectsβofβloanβagreements,βbeβitβinβtermsβofβinterestβrates,βnon-interestβrateβcharges,βloanβvolumesβandβcollateralβrequiredβfromβborrowersβ(seeβchart 7).βSMEsβthereforeβdoβnotβseemβtoβhaveβbeenβpenalisedβinβanyβwayβasβregardsβlendingβconditionsβcomparedβtoβtheβtermsβappliedβtoβlargeβfirmsβbetween 2016βand 2019.
Chart 7
Firmsβ assessment of credit criteria : breakdown by size 1
(balanceβofβpercentagesβofβresponsesβreportingβimprovementβ(+)βorβdeteriorationβ(β),4-quarterβmovingβaverage)
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β30
β20
β10
0
10
20
30
40
50
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β35
β30
β25
β20
β15
β10
β5
0
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β35
β30
β25
β20
β15
β10
β5
0
5
10
15
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
β45
β40
β35
β30
β25
β20
β15
β10
β5
0
5
Small firms
Interest rates
Medium-sized firms Large firms Very large firms
Non-interest rate charges
Collateral requirementsSize of the loan
Sourceβ:βNBBβ(Quarterlyβsurveyβofβcorporateβcreditβconditions).1β Smallβ=β1-49 workersβ;βmediumβ=β50-249 workersβ;βlargeβ=β250-499 workersβ;βveryβlargeβ=β500 workersβorβmore.
10NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
2. Econometric analysis
Theβdevelopmentsβdescribedβinβtheβpreviousβsectionβareβattributableβtoβaβcombinationβofβvariousβfactors.β Inβparticular,βtheβexpansionβofβcorporateβcreditβisβnaturallyβinfluencedβpartlyβbyβtheβmovementβinβinterestβratesβchargedβbyβbanks.βAsβ theβdescriptiveβ statisticsβ suggest,β itβmayβalsoβbeβgreatlyβ influencedβbyβ theβeconomicβclimateβviaβbothβdemandβeffectsβandβsupplyβeffects.βDemandβeffectsβariseβinβparticularβbecauseβfirmsβinvestβmoreβ(less)β inβperiodsβofβstrongβ(weak)βactivity,βandβbecauseβofβtheβprocyclicalityβofβtheirβ liquidityβneeds,βasβpurchasesβofβintermediateβgoodsβandβwageβpaymentsβmoveβinβlineβwithβeconomicβactivityβandβemployment.βInβ addition,β asβ aβ deterioratingβ economicβ situationβ isβ accompaniedβ byβ anβ increasedβ creditβ riskβ concerningβsomeβ firms,β banksβ tendβ toβ tightenβ restrictionsβ onβ thoseβ whichβ areβ struggling,β e.g.β byβ imposingβ higherβriskβ premiums.β Regardingβ interestβ rateβ levelsβ andβ trends,βweβ haveβ seenβ earlierβ thatβ theyβ closelyβ trackβ theβmovementβinβbanksββfundingβcosts,βwhichβareβthemselvesβdeterminedβmainlyβbyβtheβinterestβratesβprevailingβonβtheβmoneyβmarket.
Asβ alreadyβ pointedβout,β identifyingβhowβ theβ Lawβofβ 21 December 2013 hasβ affectedβ lendingβ volumesβ andβinterestβrateβlevelsβentailsβisolatingβitsβimpact,βifβany,βfromβthatβofβtheβotherβfactorsβmentionedβabove.βHowever,βsuchβ anβ exerciseβ isβ notβ easyβ sinceβ thatβ lawβmayβ influenceβ businessβ creditβ inβ variousβwaysβ:β bothβ positively,βbyβ strengtheningβ competitionβ betweenβ creditβ institutions,β promptingβ themβ toβ adaptβ theirβ tariffβ strategiesβtoβmakeβ themβmoreβ favourableβ toβ borrowers,β andβ negatively,β byβ regulatingβ theβ fixingβ ofβ theβ prepaymentβpenalty,β perhapsβ causingβ banksβ toβ includeβ anβ excessβ riskβ premiumβ inβ borrowingβ rates.β Butβ theseβ opposingβeffectsβ areβ notβ directlyβ quantifiableβ onβ theβ basisβ ofβ theβ dataβ availableβ toβ theβ Nationalβ Bank.β Theβ chosenβapproachβthereforeβconsistedβinβexaminingβdevelopmentsβinβbusinessβcreditβandβinterestβratesβandβidentifyingβdivergencesβcomparedβtoβwhatβmightβnormallyβbeβexpectedβinβviewβofβtheβeconomicβenvironmentβandβbanksββfundingβcosts.
Thisβ meantβ conductingβ anβ econometricβ analysisβ enablingβ usβ toβ separateβ anyβ structuralβ changesβ afterβ theβbeginningβof 2014βββwhenβtheβlawβcameβintoβforceβββfromβcyclicalβeffectsβmeasuredβonβtheβbasisβofβtheβBankβsβoverallβ syntheticβ indicatorβ andβ changesβ connectedβ withβ theβ movementβ inβ fundingβ costsβ onβ theβ interbankβmarkets,βexaminedβbyβmeansβofβtheβswapβrates.βDivergencesβspecificβtoβtheβperiodβbeginningβ in 2014βwereβassessedβwithβtheβaidβofβaβbinaryβvariableβintroducedβintoβeachβmodel.βTheβvariousβequationsβtakeβtheβformβofβautoregressiveβdistributedβlagβmodels.βHowever,βtheβonesβrelatingβtoβinterestβratesβwereβconvertedβintoβerrorβcorrectionβmodelsβinβorderβtoβtakeβaccountβofβanyβlong-termβlinkβbetweenβtheβinterestβratesβchargedβbyβbanksβonβloansβtoβbusinessesβandβtheβcostβofβfundsβassessedβaccordingβtoβtheβswapβrates.
Theβ specificationsβ ofβ theβ variousβmodelsβ areβ shownβ inβ anβ annexβ (Annexβ 3),β asβ areβ theβ detailedβ resultsβ ofβtheβestimationsβ(Annexesβ4 andβ5).βTheβmodelβconcerningβ(year-on-year)βgrowthβofβlendingβtoβnon-financialβcorporationsβisβdeclinedβinβfourβversionsβ:βoneβforβallβlendingβandβthreeβothersβforβtheβvariousβsizeβcategories,βnamelyβmicroβandβsmallβfirms,βgroupedβ inβaβsingleβcategory,βandβmedium-sizedβandβ largeβfirms.βTheβmodelβconcerningβinterestβratesβwasβestimatedβforβtheβfourβweightedβaverageβratesβrepresentedβinβchart 3.
Theβ resultsβ obtainedβ (chart 8)β doβnotβ showβanyβ significantβ structuralβ changeβ inβ theβ creditβ situationβ thatβcouldβpotentiallyβbeβlinkedβtoβtheβentryβintoβforceβofβtheβlaw.βInβgeneral,βtheβoutstandingβamountβofβloansβtoβbusinessesβfollowsβaβrelativelyβstableβtrendβalthoughβitβisβactuallyβinfluencedβbyβcyclicalβdevelopmentsβtoβsomeβextent,βandβafterβaβ timeβ lag.βAsβalreadyβ shownβbyβ theβdescriptiveβ statisticsβ inβ theβfirstβ section,β theβcreditβ usedβ byβmicro-β andβ smallβ firmsβ isβ lessβ sensitiveβ toβ theβ economicβ environmentβ thanβ loansβ grantedβtoβ largerβfirms.βMoreover,βbankβ loansβtoβ largeβfirmsβseemβtoβbeβtheβonlyβonesβ influencedβbyβ interestβrateβchanges,βbutβtheβ impactβ isβnotβveryβsignificant.βPartβofβtheβreasonβmayβbeβthat,βunlikeβmostβSMEs,βsomeβlargeβfirmsβareβableβtoβaccessβmarketβfinancingβwhenβtheβ interestβ ratesβchargedβbyβcreditβ institutionsβareβdeemedβtooβhigh.
11NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Chart 8
Average gaps between loans and interest rates compared to their βexpectedβ trend at the beginning of 2014(effectsβspecificβtoβtheβperiod 2014-2020,βestimatedβbyβmeansβofβeconometricβmodelsβandβ95β%βconfidenceβintervals)
β6
β4
β2
0
2
4
6
8
10
12
β20
β15
β10
β5
0
5
10
Totalcredit
Smallfirms
Medium-sizedfirms
Largefirms
Varia
ble
rate
s <1
yea
r,am
ount
s <β¬
1 m
illio
n
Varia
ble
rate
s <1
yea
r,am
ount
s >β¬
1 m
illio
n
Fixe
d ra
tes
>1 y
ear
and
<5 y
ears
, am
ount
s <β¬
1 m
illio
n
Fixe
d ra
tes
>5 y
ears
, a
mou
nts
<β¬ 1
mill
ion
Estimated gaps for annual growth of credit used(percentage points)
Estimated gaps for interest rates(basis points)
Sourceβ:βNBB.Noteβ:ββTheβdashesβinβtheseβchartsβrepresentβtheβeffectsβspecificβtoβtheβperiodβfromβtheβbeginningβof 2014βtoβtheβfinalβquarterβof 2019β(forβ
loans)βorβtoβFebruary 2020β(forβinterestβrates).βTheseβeffectsβareβinterpretedβasβdeviationsβfromβwhatβtheβmodelβpredictsβsolelyβonβtheβbasisβofβtheβfundamentalβfactorsβtakenβintoβaccountβinβtheβmodels.βTheβverticalβlinesβrepresentβtheβconfidenceβintervals.βIfβtheβvalueββ0ββonβtheβy-axisβisβincludedβinβtheβinterval,βtheβassociatedβeffectβisβconsideredβinsignificantβatβaβ5β%βlevel.
Accordingβ toβ theβestimatesβobtainedβ forβmodelsβ relatingβ toβ theβ interestβ ratesβappliedβbyβbanks,β thoseβ ratesβareβdeterminedβmainlyβbyβtheβcostβofβfunds.βTheβresultsβinβfactβconfirmβthatβinβmostβcasesβtheseβinterestβratesβmirrorβ theβ trendβ inβ theβ keyβ interestβ ratesβ andβmoneyβmarketβ rates,β exceptβ forβmedium-termβ interestβ rates.βMoreover,βtheβeconomicβclimateβdoesβnotβappearβtoβhaveβaβsignificantβeffectβonβmostβinterestβrates,βatβ leastβnotβduringβtheβperiodβcoveredβbyβtheβdata.βHowever,βtheyβdidβdeviateβfromβtheβtrendβinβmoneyβmarketβratesβafterβeruptionβofβtheβcrisisβinβtheβsecondβhalfβof 2008.
Whileβborrowingβratesβcontinuedβtoβfallβbetweenβtheβendβof 2014βandβtheβendβofβFebruary 2020,βthereβ isβnoβsignificantlyβnegativeβeffectβexceptβ inβ theβcaseβofβ interestβ ratesβatβmoreβ thanβoneβyear.βThatβchangeβprobablyβreflectsβaβreductionβinβintermediationβmarginsβdueβtoβincreasedβpressureβfromβcompetition,βasβwasβalsoβrevealedβbyβtheβBankβLendingβSurveyβ(seeβabove),βratherβthanβbeingβdueβtoβtheβLawβofβ21 December 2013.
Finally,βanβanalysisβofβtheβgapsβbetweenβtheβvaluesβpredictedβbyβtheβvariousβmodelsβandβtheβobservedβvaluesβ(notβreportedβhere)βalsoβfailedβtoβshowβanyβtemporaryβeffects,βasβtheβerrorβtermsβofβtheβmodelsβrelatingβtoβcreditβandβinterestβratesβforβtheβperiodβcommencingβin 2014βremainedβwithinβtheβusualβmargins.
12NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Conclusions
ThisβarticleβreviewedβtheβdynamicsβofβtheβvolumeβofβloansβgrantedβbyβbankingβinstitutionsβoperatingβinβBelgiumβtoβresidentβenterprises,βandβtheβinterestβratesβandβotherβcreditβconditions,βsinceβtheβentryβintoβforceβofβtheβLawβofβ21 December 2013 onβSMEβfinancing.
LoansβtoβSMEsβββgenerallyβlessβprocyclicalβthanβtheβvolumeβofβcreditβusedβbyβlargeβfirmsβββincreasedβbetween 2014βandβ theβ beginningβ of 2020.β Duringβ thatβ period,β theβ growthβ ofβ lendingβ toβmicro-companiesβ andβ smallβ firmsβaveragedβ4.6β%βyear-on-year,βcomparedβtoβ0.6β%βforβmedium-sizedβfirms.βThisβsituationβisβdueβpartlyβtoβtheβcreditβsupply,βwhichβexpandedβthroughoutβtheβperiod,βsupportedβbyβtheβvariousβmeasuresβtakenβbyβtheβEurosystemβtoβstimulateβeconomicβactivity.βUpβtoβtheβendβof 2018,βtheβexpansionβofβcreditβwasβalsoβpropelledβbyβrisingβdemandβfromβbothβSMEsβandβlargeβfirms.
Interestβratesβonβbusinessβloansβareβdeterminedβmainlyβbyβinterbankβmarketβrates,βwhichβareβthemselvesβstronglyβinfluencedβbyβmonetaryβpolicy.βForβsomeβyearsβnow,βtheβEurosystemβsβmonetaryβpolicyβhasβindirectlyβdoneβmuchβtoβ facilitateβ accessβ toβ bankβ financeβ forβ businessesβ (andβ households).β Theβmeasuresβ implementedβ since 2014βhaveβcutβtheβcostβofβfundsβforβcreditβinstitutions.βTheβlatterβalsoβhaveβaccessβtoβloansβatβadvantageousβratesβviaβtheβtargetedβlonger-termβrefinancingβoperations.βFurthermore,βtheβassetβpurchaseβprogrammeβmadeβitβeasierβforβthemβtoβfreeβupβliquidityβwhichβcouldβbeβallocatedβtoβnewβloans.βFinally,βasβaβresultβofβcompetition,βtheβreductionβinβ banksββ fundingβ costsβ ledβ toβ firmsβ beingβ offeredβ lowerβmedium-termβ andβ long-termβ borrowingβ rates,β andβcausedβshort-termβratesβtoβstabiliseβatβaβlowβlevel.
Accordingβtoβvariousβsurveys,βfirmsβhaveβinβfactβreportedβthatβtheirβcreditβconditionsβhaveβimprovedβsince 2014,βandβ wereβ particularlyβ favourableβ between 2016β and 2019.β However,β owingβ toβ theβ increasedβ risks,β theseβconditionsβwereβtightenedβslightlyβatβtheβendβofβtheβperiod,βandβthatβaffectedβallβcategoriesβofβfirmsβregardlessβofβsize.
13NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Annex 1
Outstanding total of credit used by size and branch of activity(in β¬ billion, endβofβyear data)
2002 2008 2013 2016 2019
All firms 1, 2 68.4 98.4 109.7 117.0 138.9
Small firms (including micro companies) 1 29.3 45.8 54.1 59.2 70.7
of which :
Manufacturing industry 2.9 3.5 3.4 3.5 4.4
Electricity, gas, steam and air conditioning supply 0.0 0.1 0.2 0.4 0.6
Water supply ; sewerage, waste management and remediation activities 0.1 0.2 0.2 0.2 0.3
Construction 3.1 5.4 6.5 7.3 9.0
Wholesale and retail trade ; repair of motor vehicles and motor cycles 7.7 10.3 10.8 11.1 12.1
Transportation and storage 1.3 1.6 1.5 1.5 2.0
Accommodation and food service activities 1.4 1.8 2.2 2.3 2.7
Information and communication 0.5 0.8 1.1 1.2 1.4
Real estate activities 5.5 9.3 11.5 13.8 16.6
Mediumβsized firms 1 13.1 18.4 24.4 22.2 24.9
of which :
Manufacturing industry 3.5 3.1 2.8 2.8 3.1
Electricity, gas, steam and air conditioning supply 0.1 0.3 0.7 0.5 0.8
Water supply ; sewerage, waste management and remediation activities 0.5 0.5 0.4 0.3 0.3
Construction 1.2 1.9 3.1 2.5 2.9
Wholesale and retail trade ; repair of motor vehicles and motor cycles 3.4 3.6 4.5 4.4 4.3
Transportation and storage 1.0 1.3 1.4 1.3 1.5
Accommodation and food service activities 0.2 0.2 0.3 0.4 0.3
Information and communication 0.3 0.2 0.3 0.3 0.4
Real estate activities 1.3 2.7 4.1 3.9 4.6
Large firms 1 19.8 28.1 24.2 27.3 31.5
of which :
Manufacturing industry 7.4 5.1 4.0 4.3 6.0
Electricity, gas, steam and air conditioning supply 2.1 3.9 5.2 4.0 4.6
Water supply ; sewerage, waste management and remediation activities 1.0 1.9 2.4 2.2 2.2
Construction 0.8 1.1 1.3 1.3 1.5
Wholesale and retail trade ; repair of motor vehicles and motor cycles 4.8 6.3 5.7 5.9 5.1
Transportation and storage 1.0 1.3 1.6 2.3 2.0
Accommodation and food service activities 0.1 0.1 0.1 0.1 0.1
Information and communication 0.8 0.4 0.3 0.4 0.7
Real estate activities 0.3 0.2 0.2 0.5 0.5
Source : NBB (Central Corporate Credit Register).1 No account was taken of credit used by firms classified under financial services.2 The sum of the loans to small, medium and large firms is less than the total credit recorded because some of the loans are made to firms
for which no size information is available (because they have not yet filed a balance sheet or are not required to do so).
14NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Annex 2
Annualised growth rate 1 of used credit, by size and branch of activity(in %)
2002β2007 2008β2013 2014β2020 3 2002β2020 3
All firms 2 4.0 3.1 4.4 4.1
Small firms (including micro companies) 2 6.4 4.8 4.6 5.0
of which :
Manufacturing industry 1.9 0.6 3.8 2.8
Electricity, gas, steam and air conditioning supply 18.3 37.5 16.1 20.7
Water supply ; sewerage, waste management and remediation activities 2.7 6.9 5.1 5.0
Construction 7.6 6.1 5.6 6.1
Wholesale and retail trade ; repair of motor vehicles and motor cycles 3.9 2.3 1.7 2.3
Transportation and storage 1.8 1.3 5.1 3.7
Accommodation and food service activities 5.0 3.9 3.7 4.0
Information and communication 7.9 7.6 4.9 6.0
Real estate activities 8.4 6.0 6.3 6.7
Mediumβsized firms 2 1.1 7.6 0.6 2.1
of which :
Manufacturing industry β4.9 1.1 0.8 β0.2
Electricity, gas, steam and air conditioning supply 21.7 57.0 1.9 16.7
Water supply ; sewerage, waste management and remediation activities 4.4 0.7 β5.5 β2.3
Construction 1.4 8.9 0.7 2.4
Wholesale and retail trade ; repair of motor vehicles and motor cycles β1.1 5.7 β0.1 0.8
Transportation and storage 4.6 10.3 β0.3 2.8
Accommodation and food service activities 1.3 9.5 2.9 3.9
Information and communication 3.8 6.0 3.6 4.1
Real estate activities 8.5 9.4 2.7 5.2
Large firms 2 0.1 1.3 4.2 2.8
of which :
Manufacturing industry β6.5 β4.4 8.3 2.9
Electricity, gas, steam and air conditioning supply 41.6 14.1 β0.8 10.5
Water supply ; sewerage, waste management and remediation activities 8.7 9.1 0.0 3.5
Construction 4.7 8.9 5.7 6.1
Wholesale and retail trade ; repair of motor vehicles and motor cycles 0.2 2.6 β1.7 β0.5
Transportation and storage 3.1 11.2 5.5 6.2
Accommodation and food service activities 22.9 6.1 17.6 16.4
Information and communication 2.2 β2.7 15.1 9.1
Real estate activities β7.8 79.5 31.9 33.5
Source : NBB (Central Corporate Credit Register).1 The annualised growth rate is determined as follows : a 12βmonth moving average is calculated on the basis of the quarterly growth rates
(Encoursβtβ/βEncoursβtβββ1βββ1), disregarding the quarters affected by a break in the series (namely the second quarter of 2012, and the fourth quarter of 2014). This average is then annualised.
2 No account was taken of credit used by firms classified under financial services.3 Data taken into account up to February 2020.
15NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Annex 3
Description of the econometric models and detailed results
Theβautoregressiveβdistributedβlagβmodelsβdescribingβtheβrelationshipβbetweenβcreditβgrowthβonβtheβoneβhand,βandβbankβinterestβratesβandβtheβbusinessβcycleβonβtheβother,βareβdefinedβbyβtheβfollowingβequationβ:
whereβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
representsβtheβgrowthβofβusedβcreditβcomparedβtoβtheβcorrespondingβquarterβofβtheβpreviousβyearβforβaβcategoryβofβfirmsβ(microβ/βsmall,βmediumβorβlarge),β
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βisβtheβlong-termβinterestβrateβ(correspondingβtoβloansβofβlessβthanββ¬β1 millionβwithβtheβrateβinitiallyβfixedβforβmoreβthanβfiveβyears)βandβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βisβtheβstateβofβeconomicβactivityβmeasuredβ accordingβ toβ theβquarterlyβ averageβofβ theβBankβsβ overallβ syntheticβ indicator.β
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
isβ aβ binaryβ variableβ intendedβ toβ captureβ structuralβ changesβ inβ thatβ relationshipβ which,β ifβ negative,β couldβ beβattributedβtoβtheβcrisisβwhichβbeganβinβtheβthirdβquarterβof 2008.βTheβsecondβbinaryβvariable,β
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
β,βisβusedβtoβassessβanyβstructuralβchangesβspecificβtoβtheβperiodβfollowingβtheβentryβintoβforceβofβtheβlawβonβSMEβfinancing.βTheβlastβtwoβbinaryβvariables,β
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βandβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βhaveβnoβeconomicβinterpretation.βTheyβ serveβonlyβ toβabsorbβ statisticalβbreaksβdueβ toβmethodologicalβ changesβ inβ theβcompilationβofβ theβCentralβCreditβRegisterβsβstatistics.
Inβessence,βtheβmodelsβusedβtoβidentifyβtheβfactorsβdeterminingβchangesβinβinterestβratesβareβalsoβautoregressiveβdistributedβlagβmodelsβ:
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
Whereβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
nowβsymbolisesβoneβofβtheβfourβinterestβratesβillustratedβinβchart 3,βandβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βstandsβforβtheβswapβinterestβratesβ usedβ asβ theβ referenceβ forβ eachβ ofβ themβ inβ thatβ sameβ chart.β Theβ otherβ variablesβ areβ definedβ inβ theβsameβwayβasβinβtheβmodelsβrelatingβtoβusedβcredit.βUnlikeβthoseβmodels,βwhichβareβbasedβonβquarterlyβdata,βtheβmodelβrelatingβtoβinterestβratesβisβestimatedβonβtheβbasisβofβmonthlyβdata.βHowever,βitβisβnotβestimatedβasβsuchβ;βitβwasβreformulatedβasβaβgeneralisedβerrorβcorrectionβmodelβ inβorderβtoβtakeβaccountβofβtheβpossibleβexistenceβofβaβtrendβlinkβbetweenβbankβinterestβratesβonβloansβtoβbusinessesβandβswapβinterestβratesβ:
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
Whereβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
βandβ
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
.
Theβestimatesβobtainedβforβtheseβtwoβtypesβofβmodelsβonβtheβbasisβofβtheβordinaryβleastβsquaresβmethodβareβsetβoutβinβtheβfollowingβtables.
17
Annex 3 - Description of the econometric models and detailed results
The autoregressive distributed lag models describing the relationship between credit growth on the one hand, and bank interest rates and the business cycle on the other, are defined by the following equation:
Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ = ππππ + ππππΞ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘β1 + π½π½π½π½πππππ‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
3
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ3β2019ππππ4 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2019ππππ4 + πΏπΏπΏπΏ3π·π·π·π·2012ππππ2β2013ππππ1
+ πΏπΏπΏπΏ4π·π·π·π·2014ππππ4β2015ππππ3
where Ξ4πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ represents the growth of used credit compared to the corresponding quarter of the previous year for a category of firms (micro/small, medium or large), πππππ‘π‘π‘π‘ β1 is the long-term interest rate (corresponding to loans of less than β¬ 1 million with the rate initially fixed for more than five years) and πΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘ is the state of economic activity measured according to the quarterly average of the Bankβs overall synthetic indicator. π·π·π·π·2008ππππ3β2019ππππ4, is a binary variable intended to capture structural changes in that relationship which, if negative, could be attributed to the crisis which began in the third quarter of 2008. The second binary variable, π·π·π·π·2014ππππ1β2019ππππ4, is used to assess any structural changes specific to the period following the entry into force of the law on SME financing. The last two binary variables, π·π·π·π·2012ππππ2β2013ππππ1 and π·π·π·π·2014ππππ4β2015ππππ4 have no economic interpretation. They serve only to absorb statistical breaks due to methodological changes in the compilation of the Central Credit Registerβs statistics.
In essence, the models used to identify the factors determining changes in interest rates are also autoregressive distributed lag models:
πππππ‘π‘π‘π‘ = ππππ + ππππiπ‘π‘π‘π‘β1 + π½π½π½π½0π π π π π‘π‘π‘π‘ + π½π½π½π½1π π π π π‘π‘π‘π‘β1 + οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2+ πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where πππππ‘π‘π‘π‘ now symbolises one of the four interest rates illustrated in chart 3, and π π π π π‘π‘π‘π‘ stands for the swap interest rates used as the reference for each of them in that same chart. The other variables are defined in the same way as in the models relating to used credit. Unlike those models, which are based on quarterly data, the model relating to interest rates is estimated on the basis of monthly data. However, it is not estimated as such; it was reformulated as a generalised error correction model in order to take account of the possible existence of a trend link between bank interest rates on loans to businesses and swap interest rates:
Ξπππππ‘π‘π‘π‘ = ππππ + ππππβ(iπ‘π‘π‘π‘β1 β π π π π π‘π‘π‘π‘β1) + π½π½π½π½0Ξπ π π π π‘π‘π‘π‘ + πππππ π π π π‘π‘π‘π‘β1 +οΏ½πΎπΎπΎπΎπππππΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπΆπ‘π‘π‘π‘βππππ
9
ππππ=0
+ πΏπΏπΏπΏ1π·π·π·π·2008ππππ6β2020ππππ2 + πΏπΏπΏπΏ2π·π·π·π·2014ππππ1β2020ππππ2
Where ππππβ = (ππππ β 1) and ππππ = (ππππ+ π½π½π½π½0 + π½π½π½π½1 β 1).
The estimates obtained for these two types of models on the basis of the ordinary least squares method are set out in the following tables.
16NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Annex 4
Estimated parameters for equations relating to the annual growth of credit used by firms(ordinary least squares estimates over a period from the second quarter of 2003 to the final quarter of 2019)
Dependent variable : Ξ4Credt All firms Microβ and small firms
Mediumβsized firms
Large firms
ΞΌ β0.713 3.228 β7.243 β7.837
Ξ4Credtβββ1 0.619*** 0.761*** 0.560*** 0.422***
itβββ1 0.772 β0.240 2.112 2.445*
Conjt 0.047 0.029 0.270 β0.018
Conjtβββ1 0.065 β0.031 β0.080 0.263
Conjtβββ2 β0.016 0.045 0.005 β0.133
Conjtβββ3 0.161* 0.038 0.215 0.475**
D2008T3βββ2019T4 0.696 β0.540 4.017* 2.119
D2014T1βββ2019T4 0.886 β0.605 1.037 4.645
D2012T2βββ2013T1 0.413 β1.033 3.603 1.254
D2014T4βββ2015T3 β1.081 β0.818 β4.788** 4.217*
Sum of longβterm elasticities of cyclical effects
1
!β οΏ½! (1 β οΏ½)β"!#$ *
0.673*** 0.341* 0.931*** 1.015***
RΒ² 0.810 0.825 0.714 0.753
Standard error of the regression 1.915 1.427 3.967 3.934
Observations 67 67 67 67
Sourceβ:ββNBBβ(CentralβCorporateβCreditβRegister).*β β ββCoefficientβsignificantβatβaβlevelβofβ10β%.**β ββCoefficientβsignificantβatβaβlevelβofβ5β%.***ββCoefficientβsignificantβatβaβlevelβofβ1β%.
17NBB Economic Review Β‘ June 2020 Β‘β BankβfinancingβforβSMEsβfromβ2014βtoβ2019:βeffectβofβchangesβinβtheβlawβonβlending
Annex 5
Estimated parameters for equations relating to interest rates on loans to nonβfinancial corporations(ordinary least squares estimates over a period from February 2003 to February 2020)
Dependent variable : Ξit Variable interest rates initially fixed
for less than 1 year
(amount β€ β¬ 1 million)
Variable interest rates initially fixed
for less than 1 year
(amount > β¬ 1 million)
Rate initially fixed for more than 1 year but less than 5 years
(amount β€ β¬ 1 million)
Rate initially fixed for more than 5 years
(amount β€ β¬ 1 million)
ΞΌ 0.446*** 0.158*** 0.109 0.214***
itβββ1 β stβββ1 β0.258*** β0.151*** β0.027 β0.104***
Ξst 0.184*** 0.245*** 0.237*** 0.137***
stβββ1 β0.019** β0.015 β0.017 β0.023**
Conjt β0.003 β0.001 0.000 β0.001
Conjtβββ1 0.007 0.009 0.011* 0.004
Conjtβββ2 0.000 0.001 0.003 0.000
Conjtβββ3 β0.001 0.002 β0.003 0.002
Conjtβββ4 β0.007 β0.010* β0.005 β0.006*
Conjtβββ5 0.002 0.004 β0.007 0.001
Conjtβββ6 0.002 0.000 0.003 β0.003
Conjtβββ7 0.000 β0.002 0.007 0.002
Conjtβββ8 0.004 0.006 β0.003 0.002
Conjtβββ9 β0.002 β0.002 0.001 0.000
D2008M7βββ2020M2 0.063*** 0.086*** 0.022 0.085***
D2014M1βββ2020M2 0.005 0.031 β0.069** β0.109***
Sum of longβterm elasticities of cyclical effects
!β οΏ½! (1 β οΏ½)β%!#$ * 0.008 0.041*** 0.304 0.009
RΒ² 0.608 0.516 0.382 0.433
Standard error of the regression 0.079 0.097 0.110 0.063
Observations 205 205 182 174
Source : NBB (Central Corporate Credit Register).* Coefficient significant at a level of 10 %.** Coefficient significant at a level of 5 %.*** Coefficient significant at a level of 1 %.